WO2025217797A1 - Method, apparatus, and system for processing information, electronic device, medium, and program product - Google Patents

Abstract

The present disclosure relates to the technical field of computers, and relates to a method, apparatus, and system for processing information, an electronic device, a medium, and a program product. The method for processing information comprises: in response to an input operation by a user on a client, acquiring link information corresponding to the input operation, the link information comprising at least one uniform resource locator; determining a tag corresponding to each uniform resource locator, the length of the tag corresponding to each uniform resource locator being smaller than the length of said uniform resource locator; using a machine learning model to process at least one tag corresponding to the at least one uniform resource locator, and obtaining intermediate information corresponding to the input operation, the intermediate information comprising the at least one tag; and sending the intermediate information to the client, so that the client, on the basis of the intermediate information, displays to the user output information corresponding to the input operation. According to the present disclosure, the smoothness of the client displaying the output information to the user can be improved, thereby improving user experience.

Description

Information processing method, device and system, electronic device, medium, program product Technical Field

The present disclosure relates to the field of computer technology, and in particular to an information processing method, device and system, electronic equipment, medium, and program product.

Background Art

In related technologies, a Uniform Resource Locator (URL) corresponding to a user's input operation on a client is obtained, and the URL is processed using a machine learning model to obtain output information corresponding to the input operation, and the output information is sent to the client for display to the user.

Summary of the Invention

This summary is provided to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

According to a first aspect of some embodiments of the present disclosure, there is provided an information processing method, including:

In response to an input operation of a user at a client, obtaining link information corresponding to the input operation, the link information including at least one uniform resource locator;

determining a tag corresponding to each uniform resource locator, wherein a length of the tag corresponding to each uniform resource locator is less than a length of each uniform resource locator;

Processing, using a machine learning model, at least one tag corresponding to the at least one uniform resource locator to obtain intermediate information corresponding to the input operation, wherein the intermediate information includes the at least one tag;

The intermediate information is sent to the client, so that the client displays output information corresponding to the input operation to the user based on the intermediate information.

According to a second aspect of some embodiments of the present disclosure, there is provided an information processing method, including:

In response to a user input operation on a client, intermediate information corresponding to the input operation is received from a server, wherein the intermediate information is obtained by the server using a machine learning model to process at least one tag corresponding to at least one uniform resource locator corresponding to the input operation. The information includes the at least one tag, and the length of the tag corresponding to each uniform resource locator is smaller than the length of each uniform resource locator;

receiving, from the server, information on a correspondence between the at least one uniform resource locator and the at least one tag;

According to the correspondence information, each tag in the intermediate information is replaced with a uniform resource locator corresponding to each tag to obtain the output information;

The output information is presented to the user so that the user can link to the uniform resource locator corresponding to each tag through the output information.

According to a third aspect of some embodiments of the present disclosure, there is provided an information processing device, including:

an acquisition module configured to, in response to an input operation of a user on a client, acquire link information corresponding to the input operation, wherein the link information includes at least one uniform resource locator (URL);

a determining module configured to determine a tag corresponding to each uniform resource locator, wherein a length of the tag corresponding to each uniform resource locator is less than a length of each uniform resource locator;

a processing module configured to process at least one tag corresponding to the at least one uniform resource locator using a machine learning model to obtain intermediate information corresponding to the input operation, wherein the intermediate information includes the at least one tag;

The sending module is configured to send the intermediate information to the client, so that the client displays output information corresponding to the input operation to the user based on the intermediate information.

According to a fourth aspect of some embodiments of the present disclosure, there is provided an information processing device, including:

a first receiving module configured to, in response to a user input operation on the client, receive intermediate information corresponding to the input operation from a server, wherein the intermediate information is obtained by the server processing at least one tag corresponding to at least one uniform resource locator corresponding to the input operation using a machine learning model, the intermediate information includes the at least one tag, and a length of the tag corresponding to each uniform resource locator is less than a length of each uniform resource locator;

A second receiving module is configured to receive, from the server, information about a correspondence between the at least one uniform resource locator and the at least one tag;

a replacement module configured to replace each tag in the intermediate information with a uniform resource locator corresponding to each tag according to the correspondence information, to obtain the output information;

A display module is configured to display the output information to the user so that the user can The information is linked to the Uniform Resource Locator corresponding to each tag.

According to a fifth aspect of some embodiments of the present disclosure, there is provided an information processing system, including:

A server configured to execute the information processing method described in the first aspect;

The client is configured to execute the information processing method described in the second aspect above.

According to a sixth aspect of some embodiments of the present disclosure, an electronic device is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute the information processing method of any embodiment described in the present disclosure based on instructions stored in the memory.

According to a seventh aspect of some embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the information processing method of any embodiment described in the present disclosure is performed.

According to an eighth aspect of some embodiments of the present disclosure, a computer program product is provided, which, when executed on a computer, enables the computer to implement an information processing method.

Other features, aspects and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The preferred embodiments of the present disclosure are described below with reference to the accompanying drawings. The drawings described herein are used to provide a further understanding of the present disclosure. Each of the drawings, together with the following detailed description, is included in this specification and forms a part of the specification to explain the present disclosure. It should be understood that the drawings described below only relate to some embodiments of the present disclosure and do not constitute a limitation of the present disclosure. In the drawings:

FIG1 is a flow chart illustrating an information processing method according to some embodiments of the present disclosure;

FIG2A is a flow chart illustrating an information processing method according to other embodiments of the present disclosure;

FIG2B is a flow chart illustrating an information processing method according to yet other embodiments of the present disclosure;

FIG2C is a flow chart illustrating an information processing method according to yet other embodiments of the present disclosure;

FIG3 is a flow chart illustrating an information processing method according to yet other embodiments of the present disclosure;

FIG4 is a flow chart illustrating an information processing method according to yet other embodiments of the present disclosure;

FIG5 is a flow chart illustrating an information processing method according to yet other embodiments of the present disclosure;

FIG6 is a flow chart illustrating an information processing method according to yet other embodiments of the present disclosure;

FIG7 is a block diagram illustrating an information processing device according to some embodiments of the present disclosure;

FIG8 is a block diagram illustrating an information processing device according to other embodiments of the present disclosure;

FIG9 is a block diagram illustrating an information processing system according to some embodiments of the present disclosure;

FIG10 is a block diagram illustrating an electronic device according to some embodiments of the present disclosure;

FIG11 shows a block diagram of an electronic device according to some further embodiments of the present disclosure.

It should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not necessarily drawn to scale. The same or similar reference numerals are used throughout the drawings to indicate the same or similar parts. Therefore, once an item is defined in one drawing, it may not be discussed further in subsequent drawings.

DETAILED DESCRIPTION

The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. However, it is obvious that the embodiments described are only some embodiments of the present disclosure, rather than all embodiments. The following description of the embodiments is actually only illustrative and is in no way intended to limit the present disclosure and its application or use. It should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein.

It should be understood that the various steps described in the method embodiments of the present disclosure can be performed in different orders and/or performed in parallel. In addition, the method embodiments may include additional steps and/or omit the steps shown. The scope of the present disclosure is not limited in this respect. Unless otherwise specifically stated, the relative arrangement, numerical expressions and numerical values of the parts and steps set forth in these embodiments should be interpreted as being merely exemplary and do not limit the scope of the present disclosure.

As used in this disclosure, the term "include" and its variations are intended to be open-ended terms that include at least the following elements/features but do not exclude other elements/features, i.e., "including but not limited to." Furthermore, the term "comprise" and its variations are intended to be open-ended terms that include at least the following elements/features but do not exclude other elements/features, i.e., "including but not limited to." Therefore, "include" and "include" are synonymous. The term "based on" means "based, at least in part, on."

Reference throughout this specification to "one embodiment," "some embodiments," or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. For example, the term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Furthermore, the appearances of the phrases "in one embodiment," "in some embodiments," or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment, but may.

It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to refer to different devices, modules or The distinction between the units is not intended to limit the order or interdependence of the functions performed by these devices, modules or units. Unless otherwise specified, the concepts of "first", "second", etc. are not intended to imply that the objects described in this way must be in a given order in time, space, ranking or any other manner.

It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

The following detailed description of the embodiments of the present disclosure is provided in conjunction with the accompanying drawings, but the present disclosure is not limited to these specific embodiments. The following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. In addition, in one or more embodiments, specific features, structures, or characteristics may be combined in any suitable manner that will be apparent to those skilled in the art from this disclosure.

In related technologies, machine learning models directly process uniform resource locators, and the same resource locator is usually a long text. This will cause the machine learning model to process the uniform resource locator to generate output information relatively slowly, which in turn causes the client to easily freeze when displaying the output information to the user, resulting in a poor user experience.

The present disclosure provides a technical solution that can improve the fluency of a client displaying output information to a user and enhance the user experience.

Figure 1 is a flow chart illustrating an information processing method according to some embodiments of the present disclosure. The information processing method of this embodiment can be executed on a server.

As shown in FIG1 , the information processing method includes: step S110, in response to a user input operation on a client, obtaining link information corresponding to the input operation, the link information including at least one uniform resource locator; step S120, determining a tag corresponding to each uniform resource locator, wherein the length of the tag corresponding to each uniform resource locator is less than the length of each uniform resource locator; step S130, using a machine learning model, processing at least one tag corresponding to the at least one uniform resource locator to obtain intermediate information corresponding to the input operation, wherein the intermediate information includes the at least one tag; and step S140, sending the intermediate information to the client, so that the client displays output information corresponding to the input operation to the user based on the intermediate information. The tag corresponding to each uniform resource locator can also be called a short tag.

In the above embodiment, since the processing process of the machine learning model is usually complicated, mapping the uniform resource locator to a tag whose length is less than the length of the uniform resource locator can reduce the complexity of the machine learning model processing, thereby improving the processing efficiency of the machine learning model, and further improving the fluency of the client in displaying the output information to the user, thereby improving the accuracy of the machine learning model processing. Improve user experience.

The information processing method in other embodiments of the present disclosure will be described in detail below with reference to FIG. 2A to FIG. 5 .

Figure 2A is a schematic flow chart illustrating information processing methods according to other embodiments of the present disclosure. Figure 2A differs from Figure 1 in that steps S131A to S132A in Figure 2A are an implementation of step S130 in Figure 1. The following describes only the differences between Figure 2A and Figure 1, and the similarities are not repeated.

As shown in FIG2A , in step S131A, the at least one tag is sent to the machine learning model.

In step S132A, the intermediate information obtained by the machine learning model processing the at least one label is received.

Machine learning models include, for example, Large Language Models (LLM) or other Natural Language Processing (NLP) models.

Figure 2B is a flowchart illustrating an information processing method according to further embodiments of the present disclosure. Figure 2B differs from Figure 1 in that step S131B in Figure 2B is an implementation of step S130 in Figure 1. The following describes only the differences between Figure 2B and Figure 1, and the similarities are not repeated.

The link information also includes link text corresponding to each uniform resource locator. As shown in Figure 2B, in step S131B, the at least one tag and the link text corresponding to each uniform resource locator are processed using a machine learning model to obtain the intermediate information, wherein the intermediate information also includes the link text corresponding to each uniform resource locator. The link text is the text displayed by the client to the user and is used by the user to link to the uniform resource locator corresponding to the link text.

The link text corresponding to each URL is displayed to the user and is clickable. In response to the user clicking on the text, the user can access the URL corresponding to the text. Typically, the link text may reflect the content of the target page corresponding to the URL to which the link text links. Displaying the link text to the user can further enhance the user experience.

Figure 2C is a schematic flow chart illustrating an information processing method according to further embodiments of the present disclosure. Figure 2C differs from Figure 1 in that steps S131C to S134C in Figure 2C are an implementation of step S130 in Figure 1. The following will only describe the differences between Figure 2C and Figure 1, and the similarities will not be repeated.

As shown in FIG2C , in step S131C, in response to the machine learning model requesting to access the uniform resource locator corresponding to any tag, the any tag is replaced with the uniform resource locator corresponding to the any tag. In step S132C, the uniform resource locator corresponding to the any tag is accessed. In step S133C, the relevant content in the accessed uniform resource locator is sent to the machine learning model for the machine learning model to use. The learning model obtains the intermediate information. In step S134C, the intermediate information obtained by the machine learning model processing the at least one tag and the relevant content in the accessed uniform resource locator is received.

In the above embodiment, when the machine learning model needs it, the machine learning model can access the relevant content in the uniform resource locator, further enriching the intermediate information generated by the machine learning model, making it easier for the user of the client to distinguish the difference in the content of the target pages linked to different uniform resource locators, and further improving the user experience.

FIG3 is a flow chart illustrating information processing methods according to further embodiments of the present disclosure. FIG3 differs from FIG1 in that FIG3 illustrates another step S150 of the information processing methods according to further embodiments. The following will only describe the differences between FIG3 and FIG1, and will not reiterate the similarities.

As shown in FIG3 , in step S150, information about the correspondence between the at least one uniform resource locator and the at least one tag is sent to the client. The correspondence information is used by the client to replace each tag in the intermediate information with the uniform resource locator corresponding to each tag, thereby obtaining the output information. The information about the correspondence between the at least one uniform resource locator and the at least one tag can be represented in any reasonable form, including but not limited to a table, an icon, or a map.

Step S150 in Figure 3 is executed after step S120. There is no strict restriction on the execution order between step S150, step S130, and step S140 in the present disclosure, that is, step S150 can be executed before step S130, between step S130 and step S140, after step S140, or simultaneously with step S130 and/or step S140.

The following will describe the information processing method in some embodiments of the present disclosure from another perspective based on FIG. 4 .

Fig. 4 is a flow chart illustrating an information processing method according to yet another embodiment of the present disclosure. The information processing method of this embodiment can be executed on a client.

As shown in FIG4 , the information processing method includes: step S240, in response to the user's input operation on the client, receiving intermediate information corresponding to the input operation from the server, wherein the intermediate information is obtained by the server using a machine learning model to process at least one uniform resource locator corresponding to the input operation and at least one tag corresponding to the uniform resource locator, the intermediate information includes the at least one tag, and the length of the tag corresponding to each uniform resource locator is less than the length of each uniform resource locator; step S250, receiving the correspondence relationship information between the at least one uniform resource locator and the at least one tag from the server; step S260, replacing each tag in the intermediate information with a tag corresponding to each tag according to the correspondence relationship information. The corresponding uniform resource locator is recorded to obtain the output information; step S270, the output information is displayed to the user, so that the user can link to the uniform resource locator corresponding to each mark through the output information.

FIG5 is a flow chart illustrating an information processing method according to yet another embodiment of the present disclosure. FIG5 differs from FIG4 in that step S271 of FIG5 is an implementation of step S270 of FIG4. The following will only describe the differences between FIG5 and FIG4, and the similarities will not be repeated.

The intermediate information includes a link text corresponding to each uniform resource locator. As shown in FIG5 , in step S271 , the link text corresponding to the at least one uniform resource locator is displayed to the user, wherein the link text is used by the user to link to the uniform resource locator corresponding to the link text.

The link text corresponding to each URL is displayed to the user and is clickable. In response to the user clicking on the text, the user can access the URL corresponding to the text. Typically, the link text may reflect the content of the target page corresponding to the URL to which the link text links. Displaying the link text to the user can further enhance the user experience.

In some embodiments, the information processing method of the present disclosure may further include displaying the content in the uniform resource locator corresponding to any link text to the user in response to the user clicking on any link text.

In some embodiments, the tag in any of the aforementioned embodiments is a short tag and its length is less than a length threshold, and the length threshold is less than the length of the uniform resource locator corresponding to the tag.

In some embodiments, the machine learning model in any of the foregoing embodiments can be deployed on the server side, or on other devices or equipment outside the server side.

In some embodiments, the link information corresponding to the user's input operation can be obtained by calling a search plug-in. The link text in the aforementioned link information is, for example, Markdown link text.

The following will use Figure 6 to more intuitively describe the interaction process between the client, server, and machine learning model.

FIG6 is a flowchart illustrating an information processing method according to yet other embodiments of the present disclosure.

As shown in FIG6 , the information processing method includes steps S300 to S370 .

In step S300, the client sends the user's input information to the server. The input information instructs the server to obtain a uniform resource locator. The input information can be information obtained by the client through parsing the user's input operation on the client, or it can be information directly entered by the user. The user's input operation can be, for example, text input operation or gesture input operation, which is not limited in this disclosure.

Taking the model question-answering scenario as an example, the user opens the client application, enters the dialogue page with the agent, and enters a text message indicating a need or question on the dialogue page: "Please provide a website link for learning English." The user enters The information is used to instruct the server to obtain the uniform resource locator for learning English.

In step S310, the server invokes the search plug-in to obtain link information corresponding to the input information through the search. The link information includes at least one uniform resource locator. In some embodiments, the link information may also include link text corresponding to each uniform resource locator. The link text may be, for example, Markdown link text.

Taking the model question-and-answer scenario as an example, the server invokes a pre-deployed search plug-in to search for link information corresponding to the user's input on the conversation page, "Please provide a link to a website for learning English." This link information includes the uniform resource locator of at least one website for learning English. For example, the link information may also include link text corresponding to the uniform resource locator of each English learning website. The link text may be, for example, a title naming the website, including but not limited to "Word Learning Website" or "Grammar Learning Website."

In step S320, the server maps each uniform resource locator to a tag. The length of the tag is less than the length of the uniform resource locator corresponding to the tag. For example, the tag is a short tag and its length is less than a length threshold, and the length threshold is less than the length of the uniform resource locator corresponding to the tag.

Taking the model question-answering scenario as an example, the server maps the uniform resource locator of an English learning website to the tag $u1$, and maps the uniform resource locator of another English learning website to the tag $u2$. The numbers used here to distinguish the uniform resource locators of different websites are only used as an example and do not constitute a specific limitation to this disclosure.

In step S331, the server sends at least one tag mapped to at least one uniform resource locator to the machine learning model. The machine learning model in Figure 6 is deployed on the server as an example only. In some embodiments, the machine learning model can also be deployed on other devices or equipment other than the server. The present disclosure does not specifically limit the deployment location of the machine learning model. In some embodiments, when the link information includes link text, the server also sends the link text corresponding to each uniform resource locator to the machine learning model. The link text is, for example, Markdown link text.

Taking the model question-answering scenario as an example, the server sends the URL-mapped tokens $u1$ and $u2$ of an English learning website, along with their corresponding link texts "Word Learning Website" and "Grammar Learning Website," to the machine learning model. Token $u1$ corresponds to the link text "Word Learning Website," and token $u2$ corresponds to the link text "Grammar Learning Website."

In step S332', the machine learning model processes the at least one tag to obtain intermediate information corresponding to the input information, where the intermediate information includes the at least one tag. In some embodiments, when the link information includes link text, the machine learning model processes the at least one tag and at least one link text corresponding to the at least one uniform resource locator to obtain intermediate information, where the intermediate information includes the at least one tag and the at least one link text.

Taking the model question-answering scenario as an example, the machine learning model learns the tags $u1$ and $u2$ and their corresponding link texts "word learning website" and "grammar learning website" respectively, and obtains the same information as the input information "Please provide a website link for learning English." The corresponding intermediate information. For example, the intermediate information is a link in Markdown syntax, that is, "[Word Learning Website]($u1$) provides a special word learning method that can help users learn more words in a short time.\n[Grammar Learning Website]($u2$) is a website that can help users learn many common grammars.""\n" is a line break character.

In step S332, the server receives intermediate information from the machine learning model. For example, in the model question-answering scenario, the server receives intermediate information from the machine learning model, such as: "[Word Learning Website] ($u1$) provides a special word learning method that helps users learn more words in a short period of time. \n[Grammar Learning Website] ($u2$) is a website that helps users learn a lot of common grammar."

In step S340, the server sends intermediate information to the client. For example, in the model question-answering scenario, the server sends the intermediate information "[Word Learning Website]($u1$) provides a special word learning method that helps users learn more words in a short period of time. \n[Grammar Learning Website]($u2$) is a website that helps users learn many common grammars." to the client.

In step S350, the server sends the correspondence between at least one uniform resource locator and at least one tag to the client. Taking model question answering as an example, the server sends the correspondence between the uniform resource locator and the aforementioned tags $u1$ and $u2$ to the client.

In step S360, the client replaces each token in the intermediate information with the corresponding uniform resource locator, obtaining output information corresponding to the input information. Taking the model question-answering scenario as an example, the client replaces the token $u1$ in the intermediate information "[Word Learning Website] ($u1$) provides a special word learning method that helps users learn more words in a short period of time. \n[Grammar Learning Website] ($u2$) is a website that helps users learn many common grammars." with the corresponding uniform resource locator, and replaces the token $u2$ with the corresponding uniform resource locator, obtaining the output information "[Word Learning Website] (first uniform resource locator) provides a special word learning method that helps users learn more words in a short period of time. \n[Grammar Learning Website] (second uniform resource locator) is a website that helps users learn many common grammars." The first and second uniform resource locators here are merely used to identify different uniform resource locators and are actually the complete website addresses corresponding to the word learning website and the grammar learning website, respectively.

In step S370, the client displays the output information to the user. In some embodiments, when the link information includes link text, the client displays the link text corresponding to each uniform resource locator to the user, and in response to the user clicking on any link text, links the user to the uniform resource locator corresponding to the link text.

Taking the model question-answering scenario as an example, the client displays the output information to the user, that is, the content displayed on the dialogue page interface between the user and the agent is "The word learning website provides a special word learning method that can help users learn in a short time. Learn more words. A grammar learning website is a website that helps users learn many common grammars. "The word learning website and grammar learning website are hyperlinks. Users can click on the words "word learning website" and "grammar learning website" on the conversation page to access the specific content of the two websites, that is, jump to the corresponding website page, and display the content of the website page to the user.

The above is an information processing method provided by some embodiments of the present disclosure. The information processing device in some embodiments of the present disclosure will be described below with reference to FIG7 to FIG8.

FIG7 is a block diagram illustrating an information processing device according to some embodiments of the present disclosure.

As shown in Figure 7, the information processing device 71 includes: an acquisition module 711, configured to obtain link information corresponding to the input operation in response to the user's input operation on the client, and the link information includes at least one uniform resource locator uniform resource locator; a determination module 712, configured to determine the tag corresponding to each uniform resource locator, wherein the length of the tag corresponding to each uniform resource locator is less than the length of each uniform resource locator; a processing module 713, configured to use a machine learning model to process at least one tag corresponding to the at least one uniform resource locator to obtain intermediate information corresponding to the input operation, wherein the intermediate information includes the at least one tag; and a sending module 714, configured to send the intermediate information to the client, so that the client displays output information corresponding to the input operation to the user based on the intermediate information.

Information processing device 71 may be configured to execute steps S110 to S140 of Figure 1. In some embodiments, information processing device 71 may also be configured to execute any of the steps of Figures 2A, 2B, 2C, and 3. In some embodiments, information processing device 72 may also be configured to execute any of the steps executed by the server as shown in Figure 6.

FIG8 is a block diagram illustrating an information processing device according to other embodiments of the present disclosure.

As shown in Figure 8, the information processing device 82 includes: a first receiving module 824, which is configured to receive intermediate information corresponding to the input operation of the user on the client from the server, wherein the intermediate information is obtained by the server using a machine learning model to process at least one uniform resource locator corresponding to the input operation and at least one tag corresponding to the uniform resource locator, and the intermediate information includes the at least one tag, and the length of the tag corresponding to each uniform resource locator is less than the length of each uniform resource locator; a second receiving module 825, which is configured to receive the correspondence information between the at least one uniform resource locator and the at least one tag from the server; a replacement module 826, which is configured to replace each tag in the intermediate information with the uniform resource locator corresponding to each tag according to the correspondence information to obtain the output information; a display module 827, which is configured to display the output information to the user. The user is then linked to the uniform resource locator corresponding to each tag through the output information.

The information processing device 82 may be used to execute steps S240 to S270 of Figure 4. In some embodiments, the information processing device 82 may be used to execute any step of Figure 5. In some embodiments, the information processing device 82 may also be used to execute any step executed by the client as shown in Figure 6.

It should be noted that the above-mentioned modules are merely logical modules divided according to the specific functions they implement, and are not intended to limit specific implementation methods. For example, they can be implemented in software, hardware, or a combination of software and hardware. In actual implementation, the above-mentioned modules can be implemented as independent physical entities, or can also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.). In addition, the above-mentioned modules are shown with dotted lines in the accompanying drawings to indicate that these modules may not actually exist, and the operations/functions they implement can be implemented by the processing circuit itself.

The above is an information processing device in some embodiments of the present disclosure. The information processing system in some embodiments of the present disclosure will be described below with reference to FIG9 .

FIG9 is a block diagram illustrating an information processing system according to some embodiments of the present disclosure.

As shown in Figure 9 , the information processing system 9 includes a server 91 and a client 92. The server 91 is configured to execute steps S110 to S140 as shown in Figure 1 . The client 92 is configured to execute steps S240 to S270 as shown in Figure 4 .

In some embodiments, the server 91 is further configured to execute any steps of FIG. 2A , FIG. 2B , FIG. 2C and FIG. 3 or any steps executed by the server in FIG. 6 .

In some embodiments, the client 92 is further configured to perform any of the steps of FIG. 5 or any of the steps performed by the client in FIG. 6 .

FIG10 is a block diagram illustrating an electronic device according to some embodiments of the present disclosure.

As shown in FIG10 , the electronic device 10 includes: a memory 101 ; and a processor 102 coupled to the memory 101 , wherein the processor 102 is configured to execute the information processing method described in any of the aforementioned embodiments based on instructions stored in the memory 101 .

The memory 101 is used to store one or more computer-readable instructions. The memory 101 may include any combination of various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory, including but not limited to random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), and flash memory. The memory 101 may, for example, store an operating system, application programs, a boot loader, a database, and other programs, and may also store various various applications and data, etc.

The processor 102 is configured to execute computer-readable instructions to implement the information processing method described in any of the aforementioned embodiments. Detailed implementations of the various steps of the information processing method can be found in the aforementioned embodiments, and the repetitive details will not be repeated here.

The processor 102 and the memory 101 can communicate with each other directly or indirectly. For example, the processor 102 and the memory 101 can communicate with each other via a network. The network can include a wireless network, a wired network, and/or any combination of wireless networks and wired networks. The processor 102 and the memory 101 can also communicate with each other via a system bus, which is not limited in this disclosure.

It should be noted that the components of the electronic device 10 shown in FIG10 are merely exemplary and non-limiting. The electronic device 10 may also have other components according to actual application requirements. The processor 102 may control other components in the electronic device 10 to perform desired functions.

The electronic device may be implemented by software, firmware and/or hardware, and may be integrated into an electronic device installed with relevant application programs.

FIG11 shows a block diagram of an electronic device according to some further embodiments of the present disclosure.

The electronic device 11 shown in FIG. 11 may be a computer system with a dedicated hardware structure, which can execute corresponding functions when a relevant application program is installed.

Electronic devices include, but are not limited to, mobile terminals such as smart phones, laptops, personal digital assistants (PDAs), tablet computers (Tablet Personal Computers, Tablet PCs), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), wearable devices, etc., as well as fixed terminals such as digital televisions, desktop computers, etc.

As shown in FIG11 , a central processing unit (CPU) 111 performs various processes according to a program stored in a read-only memory (ROM) 112 or a program loaded from a storage portion 118 to a random access memory (RAM) 113. In the RAM 113, data required when the CPU 111 performs various processes, etc., is stored as needed. The central processing unit is merely exemplary, and it may also be another type of processor, such as the various processors described above. The ROM 112, RAM 113, and storage portion 118 may be various forms of computer-readable storage media. It should be noted that although the ROM 112, RAM 113, and storage portion 118 are shown separately in FIG11 , one or more of them may be combined or located in the same or different memory or storage modules.

The CPU 111, the ROM 112, and the RAM 113 are connected to one another via a bus 114. To the bus 114, an input/output interface 115 is also connected.

The following components are connected to the input/output interface 115: an input portion 116, such as a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output portion 117, including a display, such as a cathode ray tube (CRT), liquid crystal display (LCD), speaker, vibrator, etc.; a storage portion 118, including a hard disk, magnetic tape, etc.; and a communication portion 119, including a network interface card, such as a LAN card, modem, etc. The communication portion 119 allows communication processing to be performed via a network, such as the Internet. It will be readily understood that although FIG11 shows that the various devices or modules in the electronic device 11 communicate via the bus 114, they may also communicate via a network or other means, where the network may include a wireless network, a wired network, and/or any combination of wireless and wired networks.

A drive 1110 is also connected to the input/output interface 115 as needed. A removable medium 1111 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 1110 as needed so that a computer program read therefrom is installed in the storage section 118 as needed.

When the series of processes described above is implemented by software, the program constituting the software can be installed from a network such as the Internet or a storage medium such as the removable medium 1111 .

According to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which, when running on a computer, enables the computer to implement the information processing method described in any of the aforementioned embodiments. The computer program product includes a computer program carried on a computer-readable medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 119, or installed from the storage part 118, or installed from the ROM 112. When the computer program is executed by the CPU 111, the information processing method of the embodiment of the present disclosure is executed.

It should be noted that, in the context of the present disclosure, a computer-readable medium may be a tangible medium that may contain or store a program for use by an instruction execution system, apparatus, or device or for use in conjunction with an instruction execution system, apparatus, or device.

The computer readable medium may be a computer readable storage medium, or a computer readable signal medium, or any combination of the two.

Computer-readable storage media include, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present disclosure, In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium stores a computer program that, when executed by a processor, implements the information processing method described in any of the aforementioned embodiments.

A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

In some embodiments, a computer program product is further provided. When the computer program product is run on a computer, the computer is enabled to implement the information processing method described in any of the above embodiments.

In some embodiments, a computer program is further provided, comprising: instructions, which, when executed by a processor, cause the processor to execute the information processing method of any of the above embodiments. For example, the instructions may be embodied as computer program codes.

Based on the methods, devices, or apparatuses described in any of the aforementioned embodiments, the present disclosure conducted controlled experiments on extremely short URLs and domain-based short URLs. The control group was implemented using related technologies, while the experimental groups corresponding to extremely short URLs and domain-based short URLs were implemented using the methods, devices, or apparatuses described in any of the embodiments of the present disclosure. The experiments demonstrated that the experimental groups displayed information to users more smoothly and were less prone to lag compared to the control groups.

In an embodiment of the present disclosure, computer program code for performing the operations of the present disclosure may be written in one or more programming languages or a combination thereof, including but not limited to object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to the user's computer via a network. To connect to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and/or flowchart, and the combination of the boxes in the block diagram and/or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

The functions described above may be performed at least in part by one or more hardware logic components. For example, and without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art will appreciate that the above examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Those skilled in the art will appreciate that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims (14)

An information processing method, comprising: In response to an input operation of a user at a client, obtaining link information corresponding to the input operation, the link information including at least one uniform resource locator; determining a tag corresponding to each uniform resource locator, wherein a length of the tag corresponding to each uniform resource locator is less than a length of each uniform resource locator; Processing, using a machine learning model, at least one tag corresponding to the at least one uniform resource locator to obtain intermediate information corresponding to the input operation, wherein the intermediate information includes the at least one tag; The intermediate information is sent to the client, so that the client displays output information corresponding to the input operation to the user based on the intermediate information. The information processing method according to claim 1, wherein using a machine learning model to process at least one tag corresponding to the at least one uniform resource locator to obtain intermediate information corresponding to the input operation comprises: sending the at least one tag to the machine learning model; Receive the intermediate information obtained by the machine learning model processing the at least one label. The information processing method according to claim 1 or 2, further comprising: Sending correspondence information between the at least one uniform resource locator and the at least one tag to the client, wherein the correspondence information is used by the client to replace each tag in the intermediate information with the uniform resource locator corresponding to each tag to obtain the output information. The information processing method according to claim 1 or 2, wherein the link information further includes link text corresponding to each uniform resource locator, and the at least one tag corresponding to the at least one uniform resource locator is processed using a machine learning model to obtain intermediate information corresponding to the input operation, including: The at least one tag and the link text corresponding to each uniform resource locator are processed using a machine learning model to obtain the intermediate information, wherein the intermediate information also includes the link text corresponding to each uniform resource locator. The link text corresponding to the URL is the text displayed by the client to the user and is used by the user to link to the URL corresponding to the link text. The information processing method according to claim 2, wherein using a machine learning model to process at least one tag corresponding to the at least one uniform resource locator to obtain intermediate information corresponding to the input operation comprises: In response to the machine learning model requesting access to a uniform resource locator corresponding to any tag, replacing the any tag with the uniform resource locator corresponding to the any tag; Access the uniform resource locator corresponding to any of the tags; Sending relevant content in the accessed uniform resource locator to the machine learning model, so that the machine learning model obtains the intermediate information; The intermediate information is received from the machine learning model processing the at least one tag and the related content in the accessed uniform resource locator. An information processing method, comprising: In response to an input operation by a user on a client, receiving intermediate information corresponding to the input operation from a server, wherein the intermediate information is obtained by the server processing at least one tag corresponding to at least one uniform resource locator corresponding to the input operation using a machine learning model, the intermediate information including the at least one tag, and a length of the tag corresponding to each uniform resource locator being less than a length of each uniform resource locator; receiving, from the server, information on a correspondence between the at least one uniform resource locator and the at least one tag; According to the correspondence information, each tag in the intermediate information is replaced with a uniform resource locator corresponding to each tag to obtain the output information; The output information is presented to the user so that the user can link to the uniform resource locator corresponding to each tag through the output information. The information processing method according to claim 6, wherein the intermediate information includes link text corresponding to each uniform resource locator, and presenting the output information to the user comprises: A link text corresponding to the at least one uniform resource locator is displayed to the user, wherein the link text is used by the user to link to the uniform resource locator corresponding to the link text. The information processing method according to claim 7, further comprising: In response to the user clicking on any link text, the content in the uniform resource locator corresponding to the any link text is displayed to the user. An information processing device, comprising: an acquisition module configured to, in response to an input operation of a user on a client, acquire link information corresponding to the input operation, wherein the link information includes at least one uniform resource locator (URL); a determining module configured to determine a tag corresponding to each uniform resource locator, wherein a length of the tag corresponding to each uniform resource locator is less than a length of each uniform resource locator; a processing module configured to process at least one tag corresponding to the at least one uniform resource locator using a machine learning model to obtain intermediate information corresponding to the input operation, wherein the intermediate information includes the at least one tag; The sending module is configured to send the intermediate information to the client, so that the client displays output information corresponding to the input operation to the user based on the intermediate information. An information processing device, comprising: a first receiving module configured to, in response to a user input operation on the client, receive intermediate information corresponding to the input operation from a server, wherein the intermediate information is obtained by the server processing at least one tag corresponding to at least one uniform resource locator corresponding to the input operation using a machine learning model, the intermediate information includes the at least one tag, and a length of the tag corresponding to each uniform resource locator is less than a length of each uniform resource locator; A second receiving module is configured to receive, from the server, information about a correspondence between the at least one uniform resource locator and the at least one tag; a replacement module configured to replace each tag in the intermediate information with a uniform resource locator corresponding to each tag according to the correspondence information, to obtain the output information; A display module is configured to display the output information to the user so that the user can The information is linked to the Uniform Resource Locator corresponding to each tag. An information processing system, comprising: A server configured to execute the information processing method according to any one of claims 1 to 5; The client is configured to execute the information processing method according to any one of claims 6 to 8. An electronic device, comprising: Memory; and A processor coupled to the memory, wherein the processor is configured to execute the information processing method according to any one of claims 1 to 8 based on instructions stored in the memory. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the information processing method according to any one of claims 1 to 8 is implemented. A computer program product, when running on a computer, enables the computer to implement the information processing method according to any one of claims 1 to 8.