CN106777053A - The sorting technique and device of media content - Google Patents

Abstract

The invention discloses the sorting technique and device of a kind of media content.Wherein, the method includes：Attribute information according to each media content sets the degree of association between multiple media contents；Media content is classified according to the degree of association between multiple media contents.The present invention solves the inaccurate technical problem of the media content for pushing in the prior art.

Description

Method and device for classifying media content

technical field

The present invention relates to the field of the Internet, in particular to a method and device for classifying media content.

Background technique

With the continuous development of the Internet, there is more and more information on the Internet, and media content such as images, videos, audios, and texts are continuously uploaded to the Internet, causing users to search for information on web pages, usually with single-dimensional information. The user pushes, for example, if the user searches for "Passat", the content pushed by the network to the user is the media content containing "Passat" in the text content, but this push method is not accurate, that is to say, the text contains "Passat" The media content does not necessarily meet the needs of the user. If the user also needs media content about other Volkswagen series models, he needs to re-enter the keywords.

For the problem of inaccurate media content pushed in the prior art, no effective solution has been proposed yet.

Contents of the invention

Embodiments of the present invention provide a method and device for classifying media content, so as to at least solve the technical problem of inaccurate pushed media content in the prior art.

According to an aspect of an embodiment of the present invention, a method for classifying media content is provided, including: setting the degree of association between multiple media contents according to the attribute information of each media content; The media content is classified.

Further, the attribute information of the media content includes any one or more of the following: media distribution sources, keywords, frequency of occurrence of keywords, web page views, and click-through rates.

Further, acquire attribute information of the plurality of media contents; determine a correlation coefficient corresponding to each attribute information, and set the plurality of attribute information according to the attribute information of the plurality of media contents and the correlation coefficient corresponding to each attribute information. The degree of association between each media content, wherein the association coefficient is used to determine the weight of each attribute information.

Further, the media content whose degree of association exceeds a preset value is classified into the same type of media content.

Further, when accessing any media content, other media content is acquired according to the degree of association between the accessed media content and other media content. Push the obtained other media content to the page accessing any media content.

Further, the classified media content is stored in the database.

According to another aspect of the embodiments of the present invention, there is also provided a media content classification device, including: a setting module, configured to set the degree of association between multiple media contents according to the attribute information of each media content; the classification module, It is used for classifying the media content according to the degree of association between the multiple media contents.

Further, the attribute information of the media content includes any one or more of the following: media distribution sources, keywords, frequency of occurrence of keywords, web page views, and click-through rates.

Further, the setting module includes: an acquisition submodule, configured to acquire the attribute information of the plurality of media contents; a determination submodule, configured to determine the correlation coefficient corresponding to each attribute information, and according to the plurality of media contents The attribute information and the association coefficient corresponding to each attribute information set the degree of association between the multiple media contents, wherein the association coefficient is used to determine the weight of each attribute information.

Further, the classifying module includes: a classifying sub-module, configured to classify media content whose correlation degree exceeds a preset value into the same type of media content.

Further, the device further includes: an acquisition module, configured to acquire other media content according to the degree of association between the accessed media content and other media content when accessing any media content.

Further, the device further includes: a storage module, configured to store the classified media content in a database.

In the embodiment of the present invention, the degree of association between multiple media contents is set according to the attribute information of each media content, and the media content is classified according to the degree of association among the multiple media contents. The above solution classifies the media content according to the degree of correlation between the media content, thereby dividing a large number of media content in the network into different categories, so when the user searches for the media content, it can be pushed to the user in a more targeted manner, thereby It solves the technical problem in the prior art that the user finds it inconvenient due to the large amount of media content.

Description of drawings

The accompanying drawings described here are used to provide a further understanding of the present invention and constitute a part of the application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations to the present invention. In the attached picture:

Fig. 1 is the flow chart of the classification method of media content according to the embodiment of the present invention; And

Fig. 2 is a schematic diagram of an apparatus for classifying media content according to an embodiment of the present invention.

detailed description

In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed instead, may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

Example 1

According to an embodiment of the present invention, an embodiment of a method for classifying media content is provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and , although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.

Fig. 1 is the flow chart of the classification method of media content according to the embodiment of the present invention, as shown in Fig. 1, this method comprises the steps:

Step S102, setting the degree of association between multiple media contents according to the attribute information of each media content.

Specifically, the above-mentioned media content may be media content that can be searched by users in the network, and the attributes of the above-mentioned media content may include any one or more of the following: media release source, keywords, frequency of occurrence of keywords, network views and click through rate. The degree of association between multiple media contents can be set according to the attributes of the media contents. Optionally, the more the same attribute information of the media content, the higher its degree of association.

Step S104, classifying the media content according to the degree of association between the multiple media contents.

In an optional embodiment, the degree of association may be compared with a preset threshold, and when the degree of association exceeds the preset threshold, it is determined that the multiple media contents are of the same type of media content.

After obtaining multiple classifications of media content, when pushing media content to users, it is no longer necessary to obtain all media content and retrieve the content pushed to users from all media content. Instead, one category can be obtained, and one category can be obtained to send to users. Pushed content, because the classification of media content is carried out according to the relationship between each content, so pushing media content to users from a category of media content can meet the needs of users, and the efficiency and accuracy of pushing are higher. high.

It can be seen from the above that, in the above embodiments of the present application, the degree of association between multiple media contents is set according to the attribute information of each media content, and the media content is classified according to the degree of association between the multiple media contents. The above solution classifies the media content according to the degree of correlation between the media content, thereby dividing a large number of media content in the network into different categories, so when the user searches for the media content, it can be pushed to the user in a more targeted manner, thereby The problem of inaccurate media content pushed in the prior art is solved.

Optionally, according to the above-mentioned embodiments of the present application, the attribute information of the media content includes any one or more of the following: media distribution sources, keywords, frequency of occurrence of keywords, web page views, and click-through rates.

Optionally, according to the above-mentioned embodiments of the present application, step S102, setting the degree of association between multiple media contents according to the attribute information of each media content includes:

Step S1021, acquiring attribute information of multiple media contents.

Step S1023, determine the correlation coefficient corresponding to each attribute information, and set the correlation degree between multiple media contents according to the attribute information of multiple media contents and the correlation coefficient corresponding to each attribute information, wherein, the correlation coefficient is used to determine each The weight of attribute information.

In an optional embodiment, the attribute information of a plurality of media contents includes: media distribution source, keywords, frequency of occurrence of keywords, web page views and click-through rate as an example, the correlation coefficient of the media distribution source is set as 0.4, the correlation coefficient of keywords is 0.3, the correlation coefficient of keyword appearance frequency is 0.1, the correlation coefficient of web page views is 0.1, and the correlation coefficient of click rate is 0.1. After the degree of association corresponding to the attribute information of the media content is combined with the association coefficient, the degree of association among multiple media contents is obtained.

It can be seen from the above that the above scheme of the present application combines the weight corresponding to each attribute information with the attribute information to obtain the degree of relevance among multiple media contents.

Optionally, according to the above-mentioned embodiments of the present application, step S104, classifying the media content according to the degree of association among multiple media contents includes:

Step S1041, classify the media content whose degree of association exceeds a preset value into the same type of media content.

In an optional embodiment, taking four media contents A, B, C, and D as an example, Table 1 shows the degree of association between the four media contents of A, B, C, and D. In this example, Determine the default value of 0.7, combined with Table 1, the relationship between A and B is 0.35, the relationship between A and C is 0.47, the relationship between A and D is 0.80, and the relationship between B and C The relationship between B and D is 0.72, the relationship between B and D is 0.42, and the relationship between C and D is 0.59. The relationship between A and D and B and C exceeds the preset value. Therefore, A and D is classified as one type of media content, and C and D are classified as one type of media content.

Table I

Correlation A B C D. A 1 0.35 0.47 0.80 B 0.35 1 0.72 0.42 C 0.47 0.72 1 0.59 D. 0.80 0.42 0.59 1

In another optional embodiment, each attribute information may also be used as a dimension to determine the degree of association between multiple media contents in different dimensions.

Optionally, according to the foregoing embodiments of the present application, after the media content is classified according to the association relationship of multiple media contents, the method further includes:

Step S106, when any media content is accessed, other media content is acquired according to the degree of association between the accessed media content and other media content.

Step S108, pushing the obtained other media content to the page accessing any media content.

In an optional embodiment, when accessing a certain media content, one or more media contents with the highest degree of correlation with the accessed content are searched in the category to which the accessed media content belongs, and the acquired content related to the accessed content is One or more media contents with the highest correlation are pushed to the page accessing the media contents.

Optionally, according to the foregoing embodiments of the present application, after the media content is classified according to the association relationship of multiple media contents, the method further includes:

Step S1010, storing the classified media content in the database.

Specifically, in the above steps, the classified media content is stored in the database according to the classification results, and when the user performs a search, the media content is directly obtained from the database and pushed to the user.

Example 2

According to an embodiment of the present invention, an embodiment of a device for classifying media content is provided. FIG. 2 is a schematic diagram of a device for classifying media content according to an embodiment of the present invention. As shown in FIG. 2 , the device includes:

A setting module 20, configured to set the degree of association between multiple media contents according to the attribute information of each media content.

Specifically, the above-mentioned media content may be media content that can be searched by users in the network, and the attributes of the above-mentioned media content may include any one or more of the following: media release source, keywords, frequency of occurrence of keywords, network views and click through rate. The degree of association between multiple media contents can be set according to the attributes of the media contents. Optionally, the more the same attribute information of the media content, the higher its degree of association.

The classification module 22 is configured to classify the media content according to the degree of association among multiple media contents.

In an optional embodiment, the degree of association may be compared with a preset threshold, and when the degree of association exceeds the preset threshold, it is determined that the multiple media contents are of the same type of media content.

It can be seen from the above that, in the above embodiments of the present application, the degree of association between multiple media contents is set according to the attribute information of each media content, and the media content is classified according to the degree of association between the multiple media contents. The above solution classifies the media content according to the degree of correlation between the media content, thereby dividing a large number of media content in the network into different categories, so when the user searches for the media content, it can be pushed to the user in a more targeted manner, thereby It solves the technical problem in the prior art that the user finds it inconvenient due to the large amount of media content.

Optionally, according to the above-mentioned embodiments of the present application, the attribute information of the media content includes any one or more of the following: media distribution sources, keywords, frequency of occurrence of keywords, web page views, and click-through rates.

Optionally, according to the above-mentioned embodiments of the present application, the above-mentioned setting module includes:

The obtaining submodule is used to obtain attribute information of multiple media contents.

The determination sub-module is used to determine the correlation coefficient corresponding to each attribute information, and set the correlation degree between multiple media contents according to the correlation coefficient corresponding to the attribute information of multiple media contents and each attribute information, wherein, the correlation coefficient is used It is used to determine the weight of each attribute information.

Optionally, according to the above-mentioned embodiments of the present application, the above-mentioned classification module includes:

The classification sub-module is used to classify the media content whose correlation degree exceeds a preset value into the same type of media content.

Optionally, according to the above-mentioned embodiments of the present application, the above-mentioned device further includes:

The acquiring module is configured to acquire other media content according to the degree of association between the accessed media content and other media content when accessing any media content.

The push module is configured to push other acquired media content to a page accessing any media content.

Optionally, according to the above-mentioned embodiments of the present application, the above-mentioned device further includes:

The storage module is used for storing the classified media content in the database.

The serial numbers of the above embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments.

In the above-mentioned embodiments of the present invention, the descriptions of each embodiment have their own emphases, and for parts not described in detail in a certain embodiment, reference may be made to relevant descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed technical content can be realized in other ways. Wherein, the device embodiments described above are only illustrative. For example, the division of the units may be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be Integrate into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of units or modules may be in electrical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to make a computer device (which may be a personal computer, server or network device, etc.) execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage media include: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes. .

The above is only a preferred embodiment of the present invention, and it should be pointed out that for those of ordinary skill in the art, some improvements and modifications can be made without departing from the principle of the present invention. It should be regarded as the protection scope of the present invention.

Claims (12)

1. A method for classifying media content, comprising: Setting the degree of association between multiple media contents according to the attribute information of each media content; The media content is classified according to the degree of association between the multiple media contents. 2. The method according to claim 1, wherein the attribute information of the media content includes any one or more of the following: media distribution sources, keywords, frequency of occurrence of keywords, network views and click-through rate. 3. The method according to claim 2, characterized in that, according to the attribute information of each media content, the degree of association between a plurality of media contents is set, comprising: Acquiring attribute information of the plurality of media contents; Determine the correlation coefficient corresponding to each attribute information, and set the degree of correlation between the plurality of media contents according to the attribute information of the multiple media contents and the correlation coefficient corresponding to each attribute information, wherein the correlation Coefficients are used to determine the weight of each attribute information. 4. The method according to any one of claims 1 to 3, wherein classifying the media content according to the degree of association between the plurality of media contents comprises: making the degree of association exceed a preset value The media contents are classified into the same type of media contents. 5. method according to claim 4, is characterized in that, after described media content is classified according to the associated relation of described multiple media content, described method also comprises: When accessing any media content, obtain other media content according to the degree of association between the accessed media content and other media content; Push the obtained other media content to the page accessing any media content. 6. The method according to claim 4, characterized in that, after the media content is classified according to the association relationship of a plurality of the media content, the method further comprises: The classified media content is stored in a database. 7. A classification device for media content, comprising: A setting module, configured to set the degree of association between multiple media contents according to the attribute information of each media content; A classification module, configured to classify the media content according to the degree of association among the plurality of media contents. 8 . The device according to claim 7 , wherein the attribute information of the media content includes any one or more of the following: media distribution sources, keywords, frequency of occurrence of keywords, web page views, and click-through rate. 9. The device according to claim 8, wherein the setting module comprises: An acquisition submodule, configured to acquire attribute information of the plurality of media contents; A determining submodule, configured to determine a correlation coefficient corresponding to each attribute information, and set the degree of correlation between the plurality of media contents according to the attribute information of the plurality of media contents and the correlation coefficient corresponding to each attribute information , wherein the correlation coefficient is used to determine the weight of each attribute information. 10. The device according to any one of claims 7 to 9, wherein the classification module comprises: The classification sub-module is used to classify the media content whose correlation degree exceeds a preset value into the same type of media content. 11. The device according to claim 10, further comprising: An acquisition module, configured to acquire other media content according to the degree of association between the accessed media content and other media content when accessing any media content; The push module is configured to push other acquired media content to a page accessing any media content. 12. The device according to claim 10, further comprising: The storage module is used for storing the classified media content in the database.