CN119311167A - Icon organization method, device, electronic device and storage medium - Google Patents
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
- G06F3/04817—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
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- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G06F16/9535—Search customisation based on user profiles and personalisation
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- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
- G06F3/04845—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
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Abstract
本公开提供了一种图标整理方法，涉及人工智能领域、金融领域或其他领域。该方法包括：响应于在预设界面的图标整理请求，经由用户授权的情况下，获取用户的历史行为数据及与所述用户关联的参考数据；基于所述历史行为数据和所述参考数据得到用户特征向量；获得N个图标组合一一对应的N个组合特征向量，其中每个图标组合包括M个图标，各图标组合之间具有不同图标内容或相同图像内容下的不同图标排序关系，N和M皆为大于或等于1的整数；基于所述用户特征向量分别与N个所述组合特征向量之间的匹配度，向所述用户推荐至少一个候选图标组合。本公开还提供了一种图标整理装置、设备、存储介质和程序产品。
The present disclosure provides an icon arrangement method, which relates to the field of artificial intelligence, finance or other fields. The method includes: in response to an icon arrangement request in a preset interface, obtaining the user's historical behavior data and reference data associated with the user with the user's authorization; obtaining a user feature vector based on the historical behavior data and the reference data; obtaining N combination feature vectors corresponding to N icon combinations, wherein each icon combination includes M icons, and each icon combination has different icon contents or different icon sorting relationships under the same image content, and N and M are both integers greater than or equal to 1; based on the matching degree between the user feature vector and the N combination feature vectors, recommending at least one candidate icon combination to the user. The present disclosure also provides an icon arrangement device, equipment, storage medium and program product.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence, financial or other fields, and more particularly, to an icon sorting method, apparatus, device, medium and program product.
Background
The intelligent interaction of the desktop or the software interface becomes an important means for improving the user experience and increasing the user viscosity, and the user experience and the viscosity can be improved while the user requirements are met. Intelligent interaction includes intelligent arrangement of icons.
In the related art, a user manually adjusts the position of at least one icon of a desktop or a software interface, thereby meeting the use requirement of the user. Or the icons are automatically ordered according to the service using time length of each icon by the clicking times of the user on each icon to replace manual arrangement of the user.
In the process of realizing the inventive concept, the inventor finds that the mode of manually adjusting the icons does not meet the requirement of intelligent interaction, but the mode of automatically arranging the icons only by using the historical times or time is more lagged, so that the current new requirements of users cannot be met, and the potential requirements of the users cannot be mined and met.
Disclosure of Invention
In view of the foregoing, the present disclosure provides icon sorting methods, apparatus, devices, media, and program products.
According to one aspect of the embodiment of the disclosure, an icon arrangement method is provided, and the method comprises the steps of responding to an icon arrangement request of a preset interface, obtaining historical behavior data of a user and reference data associated with the user under the condition of user authorization, obtaining user feature vectors based on the historical behavior data and the reference data, obtaining N combined feature vectors corresponding to N icon combinations one by one, wherein each icon combination comprises M icons, different icon ordering relations under different icon contents or the same image content exist between each icon combination, N and M are integers larger than or equal to 1, and recommending at least one candidate icon combination to the user based on the matching degree between the user feature vectors and the N combined feature vectors.
According to the embodiment of the disclosure, before responding to the icon sorting request at the preset interface, the method further comprises the steps of monitoring a first dragging operation of the user on any icon displayed in the preset interface, and confirming that the icon sorting request is received when the first dragging distance of the user on the dragged icon is greater than or equal to a first preset distance.
According to the embodiment of the disclosure, if the user does not select any candidate icon combination, the method further comprises the steps of monitoring a second dragging operation of the user on any icon displayed in the preset interface, and when the second dragging distance of the user on the dragged icon is greater than or equal to a second preset distance, confirming that the icon arrangement request is received again, and recommending at least one candidate icon combination again.
According to the embodiment of the disclosure, the icon sorting operation comprises the first dragging operation and/or the second dragging operation, the reference data comprises a current icon sorting result of the preset interface responding to the icon sorting operation, and the obtaining of the user feature vector based on the historical behavior data and the reference data comprises obtaining the user feature vector based on the historical behavior data and the current icon sorting result.
According to the embodiment of the disclosure, recommending at least one candidate icon combination to the user comprises inputting the user feature vector and N combined feature vectors into a pre-trained recommendation model to obtain N matching degrees, and recommending the at least one candidate icon combination based on the N matching degrees.
According to the embodiment of the disclosure, acquiring the historical behavior data of the user comprises acquiring historical use data of the user on each icon of the preset interface and/or acquiring at least one icon searched in the historical searching operation of the user.
According to an embodiment of the present disclosure, obtaining reference data associated with the user includes obtaining a current real-time location of the user, and determining at least one of other user information, merchant information, and recommended service information within a predetermined area around the real-time location.
According to the embodiment of the disclosure, obtaining the user feature vector based on the historical behavior data and the reference data comprises determining a first weight coefficient based on the historical usage data and/or the searched at least one icon, determining a second weight coefficient based on at least one of the other user information, merchant information and recommended service information, and processing the historical behavior data and the reference data based on the first weight coefficient and the second weight coefficient respectively to obtain the user feature vector.
According to the embodiment of the disclosure, before N combined feature vectors corresponding to N icon combinations one to one are obtained, the method further comprises determining S first candidate icons based on the historical use data and/or the searched at least one icon, determining K second candidate icons based on at least one of the other user information, merchant information and recommended service information, S, K being integers which are larger than or equal to 1, and obtaining the N icon combinations based on the S first candidate icons and the K second candidate icons. 1
According to the embodiment of the disclosure, each icon in the N icon combinations serves as a corresponding business portal, the K second candidate icons are determined based on at least one operation of determining at least one second candidate icon based on historical business transactions between the user and any other user in the preset area, determining at least one second candidate icon based on historical business transactions between the user and any business in the preset area, determining at least one second candidate icon based on historical business transactions between all users and any business in the preset area, determining at least one second candidate icon based on the relevance of any business in the preset area to the corresponding business of any icon in the N icon combinations, and determining at least one second candidate icon based on the relevance of the user to recommended business in the preset area.
The embodiment of the invention provides an icon sorting device which comprises a data acquisition module, a user vector module, an icon vector module and a recommendation module, wherein the data acquisition module is used for responding to an icon sorting request of a preset interface, acquiring historical behavior data of a user and reference data associated with the user under the condition of user authorization, the user vector module is used for acquiring user feature vectors based on the historical behavior data and the reference data, the icon vector module is used for acquiring N combination feature vectors corresponding to N icon combinations one by one, each icon combination comprises M icons, different icon content or different icon sorting relations under the same image content exist between the icon combinations, N and M are integers which are larger than or equal to 1, and the recommendation module is used for recommending at least one candidate icon combination to the user based on the matching degree between the user feature vectors and the N combination feature vectors.
Another aspect of the disclosed embodiments provides an electronic device comprising one or more processors and storage means for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method as described above.
Another aspect of the disclosed embodiments also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method as described above.
Another aspect of the disclosed embodiments also provides a computer program product comprising a computer program which, when executed by a processor, implements a method as described above.
The above embodiment or embodiments have the advantages that the reference data associated with the historical behavior data of the user are further considered on the basis of the historical behavior data of the user, the user feature vector is matched with each combined feature vector by taking the icon combination as granularity, and at least one candidate icon combination is recommended for the user to select from the aspects of intelligent interaction and user requirements. According to the historical behavior data and the reference data of the users, products and services can be automatically recommended to each user in a customized mode through icon content and sequencing, the continuously changing requirements of the users are dynamically and in real time met, the potential needs of the users can be mined and met, and user experience is improved.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario diagram of an icon collation method according to an embodiment of the disclosure;
FIG. 2 schematically illustrates a flow chart of an icon sorting method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart of acknowledging receipt of an icon collation request in accordance with an embodiment of the disclosure;
FIG. 4 schematically illustrates a flow chart of re-recommendation according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart of deriving user feature vectors in accordance with an embodiment of the present disclosure;
FIG. 6 schematically illustrates a flow chart for deriving a combination of icons, according to an embodiment of the present disclosure;
FIG. 7 schematically illustrates a recommendation flow chart according to an embodiment of the disclosure;
FIG. 8 schematically illustrates a flow chart of an icon sorting method according to another embodiment of the present disclosure;
FIG. 9 schematically shows a block diagram of an icon collating device in accordance with an embodiment of the present disclosure, and
Fig. 10 schematically illustrates a block diagram of an electronic device adapted to implement an icon sorting method according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
In the technical scheme of the invention, the related user information (including but not limited to user personal information, user image information, user equipment information, such as position information and the like) and data (including but not limited to data for analysis, stored data, displayed data and the like) are information and data authorized by a user or fully authorized by all parties, and the processing of the related data such as collection, storage, use, processing, transmission, provision, disclosure, application and the like are all conducted according to the related laws and regulations and standards of related countries and regions, necessary security measures are adopted, no prejudice to the public welfare is provided, and corresponding operation inlets are provided for the user to select authorization or rejection.
Fig. 1 schematically illustrates an application scenario diagram of an icon sorting method according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example in which embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios.
As shown in fig. 1, an application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
The server 105 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud computing, network service, and middleware service.
For example, an Icon (Icon) refers to an image used in a desktop or a particular application interface to represent a certain application or application service. Icons of applications (abbreviated as APP in english) such as a client application, a web application, and the like are displayed on the desktops of the terminal devices 101, 102, 103, and one or more icons are displayed on each interface provided in the applications such as the client application, the web application, and the like, for example. Whether the desktop or an icon on an interface within the APP, can enter a particular service in response to a user's click, which can provide the corresponding product and service. The preset interfaces to which the present disclosure relates include interfaces within desktop and APP.
It should be noted that, the icon sorting method provided by the embodiment of the present invention may be executed by the terminal devices 101, 102, 103 or the server 105. Accordingly, the icon sorting apparatus provided in the embodiment of the present invention may be generally disposed in the terminal devices 101, 102, 103 or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The icon sorting method according to the embodiment of the present disclosure will be described in detail below with reference to fig. 2 to 8 based on the scenario described in fig. 1.
Fig. 2 schematically illustrates a flow chart of an icon sorting method according to an embodiment of the present disclosure.
As shown in fig. 2, the icon sorting method of this embodiment includes:
in operation S210, in response to the icon arrangement request at the preset interface, the historical behavior data of the user and the reference data associated with the user are acquired via the user authorization.
For example, in the terminal device, the preset interface may be a main interface (desktop) displayed after the terminal is started, where the main interface may be a single screen that does not support left-right sliding, or may be a multi-screen that supports left-right sliding. In the application program, the preset interface may be a main interface of the APP, where icons corresponding to a plurality of services are provided. The main interface may also support left-right sliding. Of course, when the icon arrangement request is received, the preset interface may be a blank area, or the icon may already be displayed.
For example, the icon sorting request may be triggered according to a user command, for example, the user command includes long pressing a certain area in the preset interface, drawing a preset track in the preset interface, multi-finger sliding, or a voice command, etc. The icon arrangement request may also be triggered automatically, e.g. each time the terminal is powered on, the user sets a timed arrangement task, it is detected that the user has moved to a predetermined location or an operator push of an application is received, etc.
In some embodiments, obtaining historical behavior data of the user includes obtaining historical usage data of the user for each icon of the preset interface. And/or at least one icon searched in the historical searching operation of the user is obtained.
The historical behavior data may include a series of operations that are continuous within a predetermined period of time in the past at the current time, such as clicking a balance inquiry icon to enter a balance page, then exiting the balance page to click a transfer icon, then entering the transfer page to transfer money, then exiting the transfer page to click a fund icon, entering a fund page to purchase a fund, etc., in sequence over a period of time (e.g., 5 minutes) when the user accesses an APP. The historical behavior data may also include discrete operations, such as the number of clicks on an icon per month, or clicking on individual icons at a location.
In some embodiments, obtaining reference data associated with the user includes obtaining a current real-time location of the user. At least one of other user information, merchant information, and recommended service information within a predetermined area around the real-time location is determined.
The reference data comprises real-time data acquired after the icon arrangement request is received, and the real-time data are used for analyzing and determining the current demands and the potential demands of the user. The reference data associated therewith may be determined by personal information of the user, historical behavior data, current behavior time and external environment information. The reference data is used for providing input of real-time information, so that real-time requirements of users are dynamically met, and potential requirements are mined. The degree of association may be flexibly determined by manually or automatically preset rules.
The real-time position can be, for example, the longitude and latitude of the user, and can be obtained by technical means such as GPS, wi-Fi positioning and the like. The predetermined area refers to a geographical area designated by the user, and may be a circular area, a rectangular area, or a custom-shaped area, for example, a circular area with a radius of 500 meters is set. The other user information refers to information of other users related to the current position of the user, for example, under the condition of authorization of the other users, user names, head portraits, sexes, historical behavior information, predicted real-time requirements and the like of the other users are obtained. Merchant information refers to information of merchants related to the current location of the user, such as merchant name, address, rating, offered services, etc. Recommended service information refers to recommended service information according to the current location and personal preference of the user.
Considering that the different positions of the user can generate the change of demands, the attribute of the position information is added on the basis of the historical behavior information of the user, thereby, the method and the device can dynamically track the shift and change of the geographic position when the user acts, so that the user needs can be better mined and predicted based on the position information, and accurate candidate icon combinations can be provided.
According to the embodiment of the disclosure, the information related to the user can be screened out through the user position information, and the information is sequentially combined with the icon arrangement to influence the content and the icon ordering of the icons recommended to the user, so that the icon combination meeting the user requirement is provided. The predetermined area can be determined according to the setting of the user, the query range is limited in the geographical area of interest of the user, and the relevance of information and user experience are improved.
In operation S220, a user feature vector is obtained based on the historical behavior data and the reference data.
Historical behavior data and reference data are vectorized, for example, using word2vev algorithms, thermal vector coding, neural network extraction, and the like.
In operation S230, N combined feature vectors corresponding to N icon combinations one to one are obtained, where each icon combination includes M icons, and each icon combination has different icon contents or different icon ordering relationships under the same image content, and N and M are integers greater than or equal to 1.
Each icon in the N icon combinations includes an icon that has been displayed in the preset interface and/or is not displayed in the preset interface. The N icon combinations can be obtained through exhaustive arrangement and combination of some icons, or can be obtained by firstly obtaining icons with high similarity according to user feature vectors and combining the icons. Each icon is a corresponding service entry, and the combined feature vector can be obtained according to the profile of the corresponding service of each icon, for example, any icon in one icon combination can obtain an icon vector based on the corresponding service profile, and then all icon vectors are spliced or input into a neural network model to obtain the combined feature vector.
In operation S240, at least one candidate icon combination is recommended to the user based on the degree of matching between the user feature vector and the N combined feature vectors, respectively.
Candidate icon combinations refer to combinations of icons ranked top according to matching degree, so that a group of candidate icon combinations matched with characteristics of the candidate icon combinations are recommended to a user according to individuality and preference of the user, and more individuality icon selection service is provided.
Since the icon sorting is automatically completed and recommended to the user after the icon sorting request is received, the user can select whether to accept a certain icon combination. If the icon is accepted, displaying the icon on the preset interface after the icon is selected, and compared with the prior art, the icon ordering convenience and efficiency are improved.
According to the embodiment of the disclosure, on the basis of introducing historical behavior data of a user, reference data associated with the user is further considered, and the user feature vector is matched with each combined feature vector by taking icon combinations as granularity, so that at least one candidate icon combination is recommended for the user to select from the aspects of intelligent interaction and user requirements. According to the historical behavior data and the reference data of the users, products and services can be automatically recommended to each user in a customized mode through icon content and sequencing, the continuously changing requirements of the users are dynamically and in real time met, the potential needs of the users can be mined and met, and user experience is improved.
Fig. 3 schematically illustrates a flow chart of acknowledging receipt of an icon collation request in accordance with an embodiment of the disclosure.
Before responding to an icon arrangement request at a preset interface, as shown in fig. 3, this embodiment includes:
in operation S310, a first drag operation of a user on any icon displayed in a preset interface is monitored.
In operation S320, when the first drag distance of the user to the dragged icon is greater than or equal to the first preset distance, it is confirmed that the icon sorting request is received.
The drag distance refers to the distance from the home position to the target position when the user performs a moving operation after pressing the icon for a long time. For example, the user presses one icon long and moves it to another location, the distance of this movement being the drag distance. The first preset distance is a preset distance threshold value, and is used for determining whether a user really aims at arranging the icons. And when the distance of the icon dragged by the user reaches or exceeds the first preset distance, executing related operation of icon sorting.
For example, if the drag distance exceeds four icons, the smart ordering is activated, and if the drag distance is less than four icons (not including four), the smart ordering is not activated.
According to the embodiment of the disclosure, the icon sorting request can be accurately triggered and confirmed by monitoring the dragging operation of the user on the icon and judging according to the dragging distance.
Fig. 4 schematically illustrates a flow chart of re-recommendation according to an embodiment of the present disclosure.
If the user does not select any candidate icon combination, as shown in fig. 4, the embodiment includes:
In operation S410, a second drag operation of the user on any icon displayed in the preset interface is monitored.
The user can drag the icon from one location to another by touching the screen and sliding the finger. The second drag operation may be a re-drag operation of the same icon or another icon by the user with respect to the first drag operation.
In operation S420, when the second drag distance of the user to the dragged icon is greater than or equal to the second preset distance, it is confirmed that the icon arrangement request is received again, and at least one candidate icon combination is recommended.
The second preset distance is a preset distance threshold value, and is used for determining whether the user deliberately carries out icon arrangement again. The re-triggering and confirmation of the icon sort operation may be implemented.
After the intelligent sorting is activated, if any candidate icon combination of the recommendation is not selected by the user, icon dragging is continued, recalculation is continued according to the user movement result, and the recommendation result is displayed until the user completes icon movement operation.
Through monitoring the second dragging operation of the user on the icon and according to the judgment of the second dragging distance, the icon arrangement request can be triggered and confirmed again, candidate icon combinations are recommended again, and more accurate icon arrangement service is provided.
It is understood that the first drag operation and/or the second drag operation includes an operation in which the user moves or rearranges by clicking, long pressing, and dragging the icon. For example, the user drags one application icon to another location on the screen. It may also include more complex operations performed by the user by clicking, long pressing and dragging icons, such as creating folders, merging icons, etc. For example, a user drags multiple application icons together to create a folder. The present disclosure is not limited to the drag operation, and the position of the icon may be moved by a voice command, eye tracking, or the like.
In some embodiments, the icon sorting operation includes a first drag operation and/or a second drag operation, and the reference data includes a preset interface current icon sorting result in response to the icon sorting operation. Obtaining the user feature vector based on the historical behavior data and the reference data includes obtaining the user feature vector based on the historical behavior data and the current icon ordering result.
The current icon sorting result is used as reference data, and can reflect the real-time sorting operation result of the icons by the user. This result may reflect the user's current layout preferences and goals. By using the user feature vector as input, various optimization algorithms can be applied to further optimize the results of the icon arrangement. For example, machine learning algorithms may be used to predict user preferences and behavior to better meet user needs.
According to the embodiment of the disclosure, the ordering and layout of the icons can be more in line with the current expectations and habits of the users, so that the users can find the required service more quickly, and the operation efficiency and the use convenience are improved.
Fig. 5 schematically illustrates a flow chart of deriving user feature vectors according to an embodiment of the disclosure.
As shown in fig. 5, this embodiment is one of the embodiments of operation S220, including:
In operation S510, a first weight coefficient is determined based on the historical usage data and/or the searched at least one icon.
Historical usage data refers to usage records and behavior data of the user in the past, including information about which applications to use, frequency of use, duration of use, and the like. The searched icon refers to a service icon that can be searched through a search function. The user can find out the required service icon by inputting keywords or performing voice search and the like.
For each icon, a weight coefficient of the single icon is obtained according to the specific gravity of the historical use frequency or the searched frequency accounting for the sum of the total historical use frequency and the total searching frequency, and the first weight coefficient is obtained by adding the normalized weights.
In operation S520, a second weight coefficient is determined based on at least one of the other user information, the merchant information, and the recommended service information.
For example, each sample, including at least one of other users, merchants or recommended services, is assigned a corresponding weight coefficient according to historical interaction data of each sample and the user, and the second weight coefficient is obtained by adding the samples after normalization processing. The historical interaction data can comprise data such as transfer times, transfer amounts and the like, and can be manually assigned with weight coefficients or automatically executed by presetting an assignment rule.
In operation S530, the historical behavior data and the reference data are processed based on the first weight coefficient and the second weight coefficient, respectively, to obtain a user feature vector.
For example, first, the historical behavior data and the reference data are extracted, respectively, to obtain the historical feature vector and the reference feature vector. Next, the historical feature vector is multiplied by the first weight coefficient, and the reference feature vector is multiplied by the second weight coefficient. And finally, splicing the two multiplication results to obtain the user feature vector.
The importance of the historical behavior data is emphasized or weakened according to the setting of the first weight coefficient so as to better reflect the behavior preference of the user. By multiplying the second weight coefficient with the reference data, the weight of the reference data in the user feature vector may be adjusted, emphasizing or weakening the importance of the reference data.
By using the first weight coefficient and the second weight coefficient to process the historical behavior data and the reference data, the historical behavior preference and the current behavior characteristic of the user can be comprehensively considered, and the accuracy and the effect of personalized recommendation and service can be improved by flexibly adjusting the weights of the historical behavior data and the reference data in the user characteristic vector.
Fig. 6 schematically illustrates a flow chart for deriving a combination of icons according to an embodiment of the present disclosure.
Before obtaining N combined feature vectors for which N icon combinations are one-to-one, as shown in fig. 6, this embodiment includes:
In operation S610, S first candidate icons are determined based on the historical usage data and/or the searched at least one icon.
Keywords of the user during the search, clicking conditions of the search results, and the like can be analyzed in combination with the search behavior of the user to determine icons related to the user's search intention. Icons related to the user's use preference and search intention are selected as first candidate icons according to analysis of the historical use data and the searched icons, for example, icons having higher use frequency or search frequency are selected as first candidate icons.
In operation S620, K second candidate icons are determined based on at least one of other user information, merchant information, and recommended service information, S, K being an integer greater than or equal to 1.
Other user information is collected by using the same application in a predetermined area as a basis. Information related to the merchant is collected in a predetermined area including merchant type, merchant rating, services provided by the merchant, and the like. And collecting hot recommendation, personalized recommendation and high-user evaluation service.
And according to the collected other user information, merchant information and recommended service information, performing comprehensive analysis by using an algorithm or a model to determine an icon matched with the personal characteristics and recommended preferences of the user as a second candidate icon, for example, the service provided by the merchant information has higher evaluation, and the service is related to a certain service, and the service is taken as the second candidate icon.
In operation S630, N icon combinations are obtained based on the S first candidate icons and the K second candidate icons.
A plurality of first candidate targets and a plurality of second candidate targets are rank-combined. Different combinations are obtained by giving different weights to the frequency of use of the user, other users and merchant information. For example, in one combination, the merchant familia is made significant and then the associated business is determined based on the service provided by the merchant, with its icon placed in a significant location. In another combination, the recommended service is weighted, and the icon of the recommended service is placed in an important position. The important position is not the first icon position, but is placed in a preset area, such as an area that is convenient for the user to click, according to the usage habit of the user.
By considering the use frequency and preference of the users and the current information of other users and merchants, more comprehensive and matched candidate icons can be provided for each user, so that personalized recommendation is realized. Based on the personalized recommendation and the determination of the important position, the user can be guided to participate in the application more, and the participation degree and viscosity of the user are improved.
In some embodiments, each of the N combinations of icons serves as a corresponding service portal, and the K second candidate icons are determined based on at least one of:
At least one second candidate icon is determined based on historical traffic between the user and any other user within the predetermined area. For example, if a user frequently transfers with other users, an icon of the transfer service may be used as a second candidate icon.
At least one second candidate icon is determined based on historical business transactions between the user and any merchant within the predetermined area. For example, if a user frequently shops or uses his service at a store, a preferential service that can be used at the merchant can be used as a second candidate icon.
At least one second candidate icon is determined based on historical business transactions between all users to any merchant within the predetermined area. For example, if a merchant is often selected or accessed by most users, the offer that can be used at that merchant may also be used as a second candidate icon.
At least one second candidate icon is determined based on the association of any business in the predetermined area with any icon in the N icon combinations. For example, if a business's service is highly correlated with a preferential service corresponding to an icon, the icon may be considered a second candidate icon.
At least one second candidate icon is determined based on the user's association with the recommended service within the predetermined area. For example, if a user's financial status and consumption habits are highly matched with the loan services hosted by banks in a predetermined area, the system may recommend the loan services to the user as a second candidate icon.
A second candidate icon associated with the icon combination is determined by considering historical business transactions of the user, business transactions of the merchant, relevance of the business, and relevance of the recommended business. A second candidate icon that is more personalized and meets the needs of the user may be provided from multiple dimensions, and a combination of icons that is more personalized and meets the needs of the user may be provided.
Fig. 7 schematically illustrates a recommendation flow chart according to an embodiment of the disclosure.
As shown in fig. 7, this embodiment is one of the embodiments of operation S210, including:
In operation S710, the user feature vector and the N combined feature vectors are input into a pre-trained recommendation model, resulting in N matching degrees.
At least one candidate icon combination is recommended based on the magnitudes of the N matching degrees in operation S720.
In some embodiments, for example, individual icon features are extracted for each icon's corresponding business content and then stitched into a combined feature vector. The recommendation model may be caused to determine a degree of sub-matching of the user feature vector to a single icon feature. The degree of matching with each icon combination is obtained based on the sum of the plurality of sub-degrees of matching.
In other embodiments, the recommendation model is made N matches based on the steps of preprocessing, building and training the recommendation model and deploying the recommendation model as follows.
And data preprocessing, namely converting the historical behavior data and the historical reference data of the user into an interaction matrix form. The interaction matrix M ε R N×M represents the degree of interaction between user i and button combination j, where 0 represents no interaction and 1 represents interaction. For example, the process comprises high-frequency service used by the user in the APP, service searched by the user for multiple times inside and outside the APP and local area branching main pushing service. The interaction matrix is obtained by giving the three actions an adapted weight to summarize the interaction actions and the interaction degree between the user and the button combination from a global perspective.
A recommendation model is established and trained, and the recommendation model can be established according to the modes of content, collaborative filtering, matrix decomposition and the like. For example, the interaction matrix M is solved into the product of two low rank matrices U ε R N×K and V ε R M×K, M≡UV T. Where U represents the user feature vector matrix, V represents the combined feature vector matrix, and K represents the vector dimension, also referred to as "potential features". Since the matrix is mostly sparse, regularization methods can be used to avoid overfitting, such as L1 regularization and L2 regularization. The model may be optimized using the mean square error (Mean Squared Error, MSE) as a loss function. On the basis, a random gradient descent (Stochastic GRADIENT DESCENT, SGD) method is introduced in the training process to optimize.
And (3) deploying a recommendation model, namely predicting the next operation of the user by using the historical behavior of the user and the current reference data after model training is completed. Specifically, a user characteristic vector is obtained by taking the historical behavior of the user and the current reference data as input, a matching degree value combined with each icon is obtained through calculation of a recommendation model, and the previous alpha icon combination schemes are recommended to the user according to the grading size sequence. The prior alpha (alpha is more than or equal to 1) icon combination schemes are recommended to the user according to the highest score.
According to the embodiment of the disclosure, a recommendation algorithm is applied to the icon arrangement scene, and the layout of the icon rearrangement is automatically recommended from multiple dimensions, so that more targeted services are provided.
Fig. 8 schematically illustrates a flow chart of an icon sorting method according to another embodiment of the present disclosure.
As shown in fig. 8, the icon sorting method of this embodiment includes:
In operation S801, a user long press of an icon is detected.
In operation S802, the user completes the target icon movement.
In operation S803, it is determined whether the movement position is greater than 4 icon positions.
In operation S804, if yes, an intelligent ordering algorithm is activated.
In operation S805, the background retrieves the input amount and executes a recommendation algorithm.
In some embodiments, for input, when the user presses the screen for a long time, the system is considered to be unfolded and constructed from three angles of interface attractiveness, user experience and banking, and the layout of the rearranged icons is automatically recommended.
For example, the aesthetic aspect of the interface is the overall aesthetic principle of designing a software interface, and icon styles and interactive interfaces for different software arrangements are designed, and based on these arrangements, the aesthetic aspect of the icon arrangement can be ensured. Various user data are collected and analyzed, the data can comprise information such as search keywords, browsing content, using frequency, residence time and the like, meanwhile, the use records and search records of online webpages, user ends, applets and online and offline websites of a user are called, the used service and potential use service of the user are called, and user preference and interest points can be mined from the user angle based on the data, so that personalized recommendation can be better conducted. Starting from the service requirement of the time period, the main pushing service position needs to be moved forward so as to push the banking service to be actually converted.
For example, a high-frequency service used by a user in the APP, a service (predictive service) of multiple searches of the user inside and outside the APP, and a local-area branching and main pushing service may be selected as input amounts. The frequency of the three behaviors serving as the input quantity is obviously different from the proportion of the total use times, the occurrence frequencies corresponding to the behaviors are regularized and then normalized into weight coefficients under the same unit, and finally differentiated weight branches are constructed.
For a recommendation system, matrix decomposition is selected as a system application algorithm, and is an algorithm widely applied in the recommendation system, and can decompose a user-button combination interaction matrix into the product of two low-rank matrices, so that vector representation learning of the user and button combination is realized. Based on such vector representations, the user's historical behavior and reference data can be used to predict the user's next operation, characterized by a combination of icons that the user is likely to use, i.e., which icon the user next clicks to place in a preset location.
In operation S806, the highest-scoring icon combination is recommended.
The icon combination comprises each icon, an icon ordering relation and coordinate information of each icon on a preset interface.
In operation S807, a recommendation ordering icon is presented, i.e., the result of operation S806. For example, a list is popped up on a preset interface, and the list provides a plurality of icon combination options for a user to select.
In operation S808, if the user does not select the recommended icon combination, it is detected whether the user continues to move the icon. If yes, jump to execute operation S804, if not, end.
In operation S809, if the user does not move the icon more than 4 positions, it is detected whether the user continues to move the icon. If yes, jumping to execute operation S802, if not, not displaying the intelligent recommendation.
By using the icon sorting method of any embodiment above as an example on the main interface or other interfaces of the bank user side, more intelligent and personalized services can be provided for the user, so that the user viscosity is enhanced, and the user satisfaction degree and loyalty degree are improved.
First, products and services can be customized recommended for the user-side page based on the user's historical data and preferences. Taking intelligent consultation as an example, the recommendation system can recommend proper investment combinations and products for the user according to factors such as risk preference, investment target and the like of the user. Thus, the bank can provide more targeted service according to the demands of the user, so that the user obtains better return on investment, thereby increasing the loyalty and satisfaction of the user.
Second, banks can be aided in increasing the user's cross-sales. By analyzing data such as purchase history, browsing records, etc. of the user, the recommendation system can recommend related products to the user. For example, when the user performs a transfer operation on an internet bank or a mobile banking, the recommendation system may automatically recommend an icon corresponding to a financial product or an insurance product to the user. Not only can the sales of banks be increased, but also more comprehensive and personalized financial services can be provided for users.
Finally, the bank can be assisted in optimizing the user experience. By analyzing the behavior data of the user, the recommendation system can automatically identify the preference of the user and pertinently adjust the display mode and the content of the user side page. For example, when a user frequently carries out transactions on a mobile banking, the recommendation system can place functions related to the transactions at the core position of the page, so that the user can conveniently operate the mobile banking. Thus, the user can feel the attention and the careless service of the bank, and the viscosity of the user is enhanced.
In summary, the application to the bank client page can bring multiple expected effects, including improving user experience, increasing user viscosity, and promoting sales.
Based on the icon arranging method, the disclosure further provides an icon arranging device. The device will be described in detail below in connection with fig. 9.
Fig. 9 schematically shows a block diagram of a configuration of an icon sorting apparatus according to an embodiment of the present disclosure.
As shown in fig. 9, the icon arrangement apparatus 900 of this embodiment includes a data acquisition module 910, a user vector module 920, an icon vector module 930, and a recommendation module 940.
The data acquisition module 910 may perform operation S210 for acquiring, in response to the icon arrangement request at the preset interface, the historical behavior data of the user and the reference data associated with the user via the user authorization.
The user vector module 920 may perform operation S220 for obtaining a user feature vector based on the historical behavior data and the reference data.
In some embodiments, the user vector module 920 may further perform operations S510 to S530, which are not described herein.
The icon vector module 930 may perform operation S230, for obtaining N combined feature vectors corresponding to N icon combinations one by one, where each icon combination includes M icons, and each icon combination has different icon contents or different icon ordering relationships under the same image content, where N and M are integers greater than or equal to 1.
In some embodiments, the icon vector module 930 may further perform operations S610 to S630, which are not described herein.
The recommending module 940 may perform operation S240 for recommending at least one candidate icon combination to the user based on the degree of matching between the user feature vector and the N combined feature vectors, respectively.
In some embodiments, the recommendation module 940 may further execute operations S710 to S740, which are not described herein.
In some embodiments, the icon arrangement apparatus 900 may further include an icon arrangement request detection module, which may perform operations S310 to S320, and operations S410 to S420, which are not described herein.
The icon sorting apparatus 900 includes modules for performing the steps of any of the embodiments described above with respect to fig. 2-8, respectively.
It should be noted that, in the embodiment of the apparatus portion, the implementation manner, the solved technical problem, the realized function, and the achieved technical effect of each module/unit/subunit and the like are the same as or similar to the implementation manner, the solved technical problem, the realized function, and the achieved technical effect of each corresponding step in the embodiment of the method portion, and are not described herein again.
Any of the data acquisition module 910, the user vector module 920, the icon vector module 930, and the recommendation module 940 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules, according to embodiments of the present disclosure. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules.
According to embodiments of the present disclosure, at least one of the data acquisition module 910, the user vector module 920, the icon vector module 930, and the recommendation module 940 may be implemented, at least in part, as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Or at least one of the data acquisition module 910, the user vector module 920, the icon vector module 930, and the recommendation module 940 may be at least partially implemented as a computer program module that, when executed, may perform the corresponding functions.
Fig. 10 schematically illustrates a block diagram of an electronic device adapted to implement an icon sorting method according to an embodiment of the disclosure.
As shown in fig. 10, an electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage section 1008 into a Random Access Memory (RAM) 1003. The processor 1001 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 1001 may also include on-board memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM 1003, various programs and data necessary for the operation of the electronic apparatus 1000 are stored. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other by a bus 1004. The processor 1001 performs various operations of the method flow according to the embodiment of the present disclosure by executing programs in the ROM 1002 and/or the RAM 1003. Note that the program may be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in one or more memories.
According to an embodiment of the disclosure, the electronic device 1000 may also include an input/output (I/O) interface 1005, the input/output (I/O) interface 1005 also being connected to the bus 1004. The electronic device 1000 may also include one or more of the following components connected to the I/O interface 1005, including an input portion 1006 that includes a keyboard, mouse, etc. Including an output portion 1007 such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc. Including a storage portion 1008 of a hard disk or the like. And a communication section 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. The drive 1010 is also connected to the I/O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed as needed in the drive 1010, so that a computer program read out therefrom is installed as needed in the storage section 1008.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments. Or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 1002 and/or RAM 1003 and/or one or more memories other than ROM 1002 and RAM 1003 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to perform the methods provided by embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 1001. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of signals on a network medium, distributed, and downloaded and installed via the communication section 1009, and/or installed from the removable medium 1011. The computer program may comprise program code that is transmitted using any appropriate network medium, including but not limited to wireless, wireline, etc., or any suitable combination of the preceding.
In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 1009, and/or installed from the removable medium 1011. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 1001. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. These examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (13)
1. An icon sorting method, comprising:
Responding to an icon arrangement request on a preset interface, and acquiring historical behavior data of a user and reference data associated with the user under the condition of user authorization;
obtaining a user feature vector based on the historical behavior data and the reference data;
N combined feature vectors corresponding to N icon combinations one by one are obtained, wherein each icon combination comprises M icons, different icon ordering relations under different icon contents or the same image content are arranged among the icon combinations, and N and M are integers larger than or equal to 1;
And recommending at least one candidate icon combination to the user based on the matching degree between the user characteristic vector and the N combined characteristic vectors respectively.
2. The method of claim 1, wherein prior to responding to the icon collation request at the preset interface, the method further comprises:
Monitoring a first dragging operation of the user on any icon displayed in the preset interface;
And when the first dragging distance of the user to the dragged icon is greater than or equal to a first preset distance, confirming that the icon sorting request is received.
3. The method of claim 2, wherein if the user does not select any candidate icon combination, the method further comprises:
monitoring a second dragging operation of the user on any icon displayed in the preset interface;
and when the second dragging distance of the user to the dragged icon is larger than or equal to a second preset distance, confirming that the icon sorting request is received again, and recommending at least one candidate icon combination again.
4. A method according to claim 2 or 3, wherein,
The icon sorting operation comprises the first dragging operation and/or the second dragging operation, and the reference data comprises a current icon sorting result of the preset interface responding to the icon sorting operation;
obtaining a user feature vector based on the historical behavioral data and the reference data includes:
And obtaining the user feature vector based on the historical behavior data and the current icon ordering result.
5. The method of claim 1, wherein recommending at least one candidate icon combination to the user comprises:
inputting the user feature vector and N combined feature vectors into a pre-trained recommendation model to obtain N matching degrees;
Recommending the at least one candidate icon combination based on the magnitude of the N matching degrees.
6. The method of claim 1, wherein obtaining historical behavioral data of the user comprises:
Acquiring historical use data of the user on each icon of the preset interface and/or
And acquiring at least one icon searched in the historical searching operation of the user.
7. The method of claim 6, wherein obtaining reference data associated with the user comprises:
obtaining the current real-time position of the user;
At least one of other user information, merchant information, and recommended service information within a predetermined area around the real-time location is determined.
8. The method of claim 7, wherein deriving a user feature vector based on the historical behavior data and the reference data comprises:
determining a first weight coefficient based on the historical usage data and/or the searched at least one icon;
determining a second weight coefficient based on at least one of the other user information, merchant information, and recommended service information;
and respectively processing the historical behavior data and the reference data based on the first weight coefficient and the second weight coefficient to obtain the user characteristic vector.
9. The method of claim 8, wherein prior to obtaining N combined feature vectors for which N icon combinations are one-to-one, the method further comprises:
determining S first candidate icons based on the historical usage data and/or the searched at least one icon;
determining K second candidate icons based on at least one of the other user information, merchant information and recommended service information, S, K being integers greater than or equal to 1;
and obtaining the N icon combinations based on the S first candidate icons and the K second candidate icons.
10. The method of claim 9, wherein each icon of the N combinations of icons is a corresponding service portal, the K second candidate icons being determined based on at least one of:
Determining at least one second candidate icon based on historical business transactions between the user and any other user within the predetermined area;
Determining at least one second candidate icon based on historical business transactions between the user and any merchant within the predetermined area;
Determining at least one second candidate icon based on historical business transactions between all users to any merchant within the predetermined area;
determining at least one second candidate icon based on the association of any business in the preset area and any icon corresponding service in the N icon combinations;
At least one second candidate icon is determined based on the association of the user with the recommended service in the predetermined area.
11. An icon sorting apparatus comprising:
The data acquisition module is used for responding to an icon arrangement request at a preset interface and acquiring historical behavior data of a user and reference data associated with the user under the condition of user authorization;
The user vector module is used for obtaining a user characteristic vector based on the historical behavior data and the reference data;
The icon vector module is used for obtaining N combined feature vectors corresponding to N icon combinations one by one, each icon combination comprises M icons, different icon ordering relations under different icon contents or the same image content are arranged among the icon combinations, and N and M are integers larger than or equal to 1;
and the recommending module is used for recommending at least one candidate icon combination to the user based on the matching degree between the user characteristic vector and the N combined characteristic vectors.
12. An electronic device, comprising:
One or more processors;
Storage means for storing one or more programs,
Wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-10.
13. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-10.
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