Information recommendation method and device, electronic equipment and medium

Abstract

The present disclosure provides an information recommendation method, relates to the technical field of artificial intelligence, and particularly relates to the technical field of recommendation systems. The implementation scheme is: obtaining behavior data of a user in a platform, wherein the behavior data comprises first click behavior data of the user on information in the platform and a list of users' attention, and the list of users' attention comprises authors that the user pays attention to; obtaining second click behavior data of the user on information published by the authors from the first click behavior data, and determining a first correlation value between the user and the authors based on the first click behavior data and the second click behavior data; determining a second correlation value between the user and the authors based on the matching degree between the user's interest field and the field of the authors; determining an interest value of the user for the authors based on the first correlation value and the second correlation value; and determining whether to recommend information published by the authors to the user based on the interest value.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and more particularly to the field of recommendation system technology, specifically to an information recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] Artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0003] With the development of multimedia information platforms, a large number of self-media authors are sharing massive amounts of multimedia information, making personalized information recommendations to users increasingly important.

[0004] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0005] This disclosure provides an information recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

[0006] According to one aspect of this disclosure, an information recommendation method is provided, comprising: acquiring user behavior data within a platform, wherein the behavior data includes first click behavior data of the user on information within the platform and the user's following list, and wherein the following list includes authors followed by the user; acquiring second click behavior data of the user on information published by the author from the first click behavior data, and determining a first association value between the user and the author based on the first click behavior data and the second click behavior data; determining the user's interest domain based on the behavior data; determining a second association value between the user and the author based on the matching degree between the interest domain and the domain in which the author is located; determining the user's interest value for the author based on the first association value and the second association value; and determining whether to recommend information published by the author to the user based on the interest value.

[0007] According to another aspect of this disclosure, a method for training a machine learning model is provided, comprising: acquiring sample behavior data of sample users, wherein the sample behavior data includes click behavior data of the sample users on information within the platform and the sample users' follow list, and wherein the follow list includes authors followed by the sample users; marking the true interest values ​​of the sample users, the true interest values ​​representing the degree of interest of the sample users towards the authors; acquiring domain information of the authors; inputting the sample behavior data and the domain information of the authors into a machine learning model to acquire predicted interest values ​​of the sample users, wherein the predicted interest values ​​indicate the predicted degree of interest of the sample users towards the authors; calculating a loss value based on the true interest values ​​and the predicted interest values; and adjusting the parameters of the machine learning model based on the loss values.

[0008] According to another aspect of this disclosure, an information recommendation device is provided, comprising: a first acquisition module configured to acquire user behavior data within a platform, wherein the behavior data includes first click behavior data of the user on information within the platform and the user's following list, and wherein the following list includes authors followed by the user; a second acquisition module configured to acquire second click behavior data of the user on information published by the author from the first click behavior data, and determine a first association value between the user and the author based on the first click behavior data and the second click behavior data; a first determination module configured to determine the user's interest domain based on the behavior data; a second determination module configured to determine a second association value between the user and the author based on the matching degree between the interest domain and the domain in which the author is located; a third determination module configured to determine the user's interest value for the author based on the first association value and the second association value; and a fourth determination module configured to determine whether to recommend information published by the author to the user based on the interest value.

[0009] According to another aspect of this disclosure, a training apparatus for a machine learning model is provided, comprising: a third acquisition module configured to acquire sample behavior data of a sample user, wherein the sample behavior data includes click behavior data of the sample user on information within a platform and the sample user's follow list, and wherein the follow list includes authors followed by the sample user; a labeling module configured to label the sample user's true interest value, the true interest value representing the sample user's degree of interest in the author; a fourth acquisition module configured to acquire domain information of the author; a fifth acquisition module configured to input the sample behavior data and the domain information of the author into a machine learning model to acquire a predicted interest value of the sample user, wherein the predicted interest value indicates the sample user's predicted degree of interest in the author; a calculation module configured to calculate a loss value based on the true interest value and the predicted interest value; and an adjustment module configured to adjust the parameters of the machine learning model based on the loss value.

[0010] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods described above.

[0011] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform any of the methods described above.

[0012] According to another aspect of this disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements any of the methods described above.

[0013] According to one or more embodiments of this disclosure, a method for information recommendation is provided. This method determines a user's level of interest in followed authors by analyzing two data points: the degree of matching between the user's and the domains of the followed authors, and the user's click behavior data on information published by the followed authors. Based on this level of interest, it determines whether to recommend the information. Therefore, by incorporating the influence of authors on users' information consumption behavior into the information recommendation process, the accuracy of information recommendations can be improved, while simultaneously increasing the readership of authors' followers, thereby enhancing the overall click-through rate of the recommendation system.

[0014] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0015] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0016] Figure 1 A flowchart of an information recommendation method according to an embodiment of the present disclosure is shown;

[0017] Figure 2 A flowchart illustrating a method for training a machine learning model according to an embodiment of the present disclosure is shown;

[0018] Figure 3 A structural block diagram of an information recommendation device according to an embodiment of the present disclosure is shown;

[0019] Figure 4 A structural block diagram of a training apparatus for a machine learning model according to an embodiment of the present disclosure is shown; and

[0020] Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0021] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0023] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0024] In related technologies, information is recommended to users based solely on the match between the content of the information itself and the user profile, without considering the influence of the author on the user's information consumption behavior. Especially with the rise of the fan economy, users may be very interested in all kinds of information published by authors they like, and traditional recommendation systems will miss this part of the information when recommending information.

[0025] To address the aforementioned issues, this disclosure determines a user's level of interest in an author by analyzing the matching degree between the user's and the authors' followed domains and the user's behavioral data. Based on this level of interest, it then determines whether to recommend information published by the author. Therefore, by incorporating the influence of authors on users' information consumption behavior into the information recommendation process, the accuracy of information recommendations can be improved, while simultaneously increasing readership among the author's followers, thereby enhancing the overall click-through rate of the recommendation system.

[0026] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0027] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0028] Figure 1 A flowchart of an information recommendation method according to an embodiment of the present disclosure is shown. Figure 1As shown, the information recommendation method 100 includes: step S101, acquiring user behavior data within the platform, wherein the behavior data includes the user's first click behavior data on information within the platform and the user's following list, and wherein the following list includes authors followed by the user; step S102, acquiring the user's second click behavior data on information published by the author from the first click behavior data, and determining a first association value between the user and the author based on the first click behavior data and the second click behavior data; step S103, determining the user's interest domain based on the behavior data; step S104, determining a second association value between the user and the author based on the matching degree between the interest domain and the domain in which the author is located; step S105, determining the user's interest value for the author based on the first association value and the second association value; and step S106, determining whether to recommend the information published by the author to the user based on the interest value.

[0029] Understandably, users may follow authors of different types and fields for various reasons, but may only be interested in and have a consumption desire for a portion of the authors they follow. Method 100 determines the user's level of interest in the authors they follow by analyzing two data points: the degree of matching between the user's and the authors' fields of interest, and the user's click behavior data on the content published by the authors they follow. Based on this level of interest, it determines whether to recommend the information. Therefore, by incorporating the influence of authors on users' information consumption behavior into the information recommendation process, the accuracy of information recommendations can be improved, while simultaneously increasing the readership of authors' followers, thereby improving the overall click-through rate of the recommendation system.

[0030] According to some embodiments, step S102 includes: determining, based on the first click behavior data, a first click rate of the user on information within the platform within a first time period and a second click rate of the user on information within the platform within a second time period, wherein the first time period is longer than the second time period; determining, based on the second click behavior data, a third click rate of the user on information published by the author within the first time period and a fourth click rate of the user on information published by the author within the second time period; and determining the first correlation value based on the ratio of the third click rate to the first click rate and the ratio of the fourth click rate to the second click rate.

[0031] Users may develop an interest in different authors at different times, and may lose interest in authors they follow after a period of time. Therefore, behavioral data of users over different time periods can be extracted to determine changes in user behavior. Specifically, the click-through rate of content published by followed authors can be compared with the click-through rate of all information on the platform over different time periods. For example, the click-through rate of content published by followed authors over the past week and month can be obtained, and the click-through rate of all information over the past week and month can be obtained accordingly. Based on the above scheme, a first correlation value can be calculated to determine the user's interest value in the author in terms of click behavior over a certain time period.

[0032] In one example, different thresholds can be set and compared with the first correlation value to determine the user's level of interest in the author in terms of click behavior.

[0033] According to some embodiments, step S106 includes: determining a first score for the information published by the author based on the interest value, wherein the first score is used to indicate whether to recommend the information published by the author to the user. Thus, a ranking score for the information published by the author is calculated based on the user's level of interest in the author to determine whether to recommend the information published by that author.

[0034] According to some embodiments, step S106 further includes: obtaining a second score for the information published by the author, wherein the second score is obtained based on the matching degree between the content of the information published by the author and the user's behavioral data; and determining a weight value for weighting the second score based on the interest value to determine the first score.

[0035] For example, features can be extracted from the content of the information published by the author, and a matching degree can be calculated based on the user's behavioral profile to determine the second score of the information published by the author. Alternatively, the above features can be input into a traditional relevance model to directly obtain the second score.

[0036] Therefore, based on content-based ranking and scoring, the method provided in this disclosure incorporates the influence of authors on users' information consumption behavior. This ensures that the final ranking of information recommendations is a comprehensive result based on both content understanding and user interest in the author. This improves the accuracy of information recommendations, increases readership among authors' followers, and ultimately enhances the overall click-through rate of the recommendation system.

[0037] According to some embodiments, determining the weight value based on the interest value includes: determining a first sub-weight value based on the interest value in response to the interest value being greater than or equal to a threshold, wherein the first sub-weight value is greater than 1; or determining a second sub-weight value in response to the interest value being less than the threshold, wherein the second sub-weight value is equal to 1. It can be understood that when it is determined that a user is interested in the author, the first sub-weight value is used as the weight value for calculating the first score, and the second score of the information published by the author is weighted with a weight value greater than 1 to obtain a larger first score, making the information more likely to be recommended to the user. Conversely, when it is determined that a user is not interested in the author, the second sub-weight value, which has a value of 1, is used as the weight value for calculating the first score, and the decision to recommend the information is based solely on its content.

[0038] According to another aspect of this disclosure, a method for training a machine learning model for information recommendation is provided. For example... Figure 2 As shown, the training method 200 for the machine learning model includes: step S201, acquiring sample behavior data of sample users, wherein the sample behavior data includes the sample users' click behavior data on information within the platform and the sample users' follow list, and wherein the follow list includes the authors followed by the sample users; step S202, labeling the sample users' true interest values, wherein the true interest values ​​characterize the sample users' degree of interest in the authors; step S203, acquiring the domain information of the authors; step S204, inputting the sample behavior data and the domain information of the authors into the machine learning model to obtain the sample users' predicted interest values, wherein the predicted interest values ​​indicate the sample users' predicted degree of interest in the authors; step S205, calculating a loss value based on the true interest values ​​and the predicted interest values; and step S206, adjusting the parameters of the machine learning model based on the loss values.

[0039] Therefore, by constructing training samples using sample user behavior data and author domain information, labeling users' true interest values, and adjusting model parameters based on the labeled true interest values ​​and the model's output interest predictions, a machine learning model is trained to predict users' interest in authors. This prediction result can be used to calculate a ranking score for the information published by the author to determine whether to recommend this information to the user. By introducing machine learning techniques to predict users' interest in followed authors, the efficiency and accuracy of subsequent information recommendation can be improved.

[0040] According to another aspect of this disclosure, an information recommendation device is provided. For example... Figure 3As shown, the information recommendation device 300 includes: a first acquisition module 301, configured to acquire user behavior data within the platform, wherein the behavior data includes first click behavior data of the user on information within the platform and the user's following list, and wherein the following list includes authors followed by the user; a second acquisition module 302, configured to acquire second click behavior data of the user on information published by the author from the first click behavior data, and determine a first association value between the user and the author based on the first click behavior data and the second click behavior data; a first determination module 303, configured to determine the user's interest domain based on the behavior data; a second determination module 304, configured to determine a second association value between the user and the author based on the matching degree between the interest domain and the domain in which the author is located; a third determination module 305, configured to determine the user's interest value for the author based on the first association value and the second association value; and a fourth determination module 306, configured to determine whether to recommend the information published by the author to the user based on the interest value.

[0041] The operation of modules 301-306 of the information recommendation device 300 is similar to the operation of steps S101-S106 described above, and will not be repeated here.

[0042] Understandably, users may follow authors of different types and fields for various reasons, but may only be interested in and have a consumption desire for a portion of the authors they follow. The information recommendation device 300 determines the user's level of interest in the authors they follow by analyzing two data points: the degree of matching between the user's and the authors' fields of interest, and the user's click behavior data on the content published by the authors they follow. Based on this level of interest, it then determines whether to recommend information. Therefore, by incorporating the influence of authors on users' information consumption behavior into the information recommendation process, the accuracy of information recommendations can be improved, while simultaneously increasing the readership of authors' followers, thereby enhancing the overall click-through rate of the recommendation system.

[0043] According to some embodiments, the second acquisition module 302 includes: a first determining unit configured to determine, based on the first click behavior data, a first click rate of the user on information within the platform within a first time period and a second click rate of the user on information within the platform within a second time period, wherein the first time period is longer than the second time period; a second determining unit configured to determine, based on the second click behavior data, a third click rate of the user on information published by the author within the first time period and a fourth click rate of the user on information published by the author within the second time period; and a third determining unit configured to determine the first association value based on the ratio of the third click rate to the first click rate and the ratio of the fourth click rate to the second click rate.

[0044] Users may develop an interest in different authors at different times, and may lose interest in authors they follow after a period of time. Therefore, the first and second determining units can extract user behavior data at different time intervals to determine changes in user behavior. Specifically, the click-through rate of content published by followed authors can be compared with the click-through rate of all information on the platform at different time intervals. For example, the click-through rate of content published by followed authors in the past week and the past month can be obtained, and the click-through rate of all information in the past week and the past month can be obtained accordingly. The third determining unit then calculates the first correlation value to determine the user's interest value in the author in terms of click behavior over a certain time interval.

[0045] In one example, the second acquisition module 302 can also be configured to set different thresholds to compare with the first associated value to determine the user's level of interest in the author in the dimension of click behavior.

[0046] According to some embodiments, the fourth determining module 306 is further configured to: determine a first score for the information published by the author based on the interest value, wherein the first score is used to indicate whether to recommend the information published by the author to the user. Thus, the fourth determining module 306 calculates a ranking score for the information published by the author based on the user's level of interest in the author to determine whether to recommend the information published by that author.

[0047] According to some embodiments, the fourth determining module 306 includes: an acquisition unit configured to acquire a second score of the information published by the author, wherein the second score is obtained based on the matching degree between the content of the information published by the author and the user's behavioral data; and a fourth determining unit configured to determine a weight value for weighting the second score based on the interest value, so as to determine the first score.

[0048] For example, the acquisition unit can extract features from the content of the information published by the author and calculate the matching degree based on the user's behavioral profile to determine the second score of the information published by the author. Alternatively, the acquisition unit can input the aforementioned features into a traditional relevance model to directly obtain the second score.

[0049] Therefore, based on content-based ranking and scoring, the information recommendation device 300 incorporates the influence of authors on users' information consumption behavior. This ensures that the final ranking of recommended information is based on a combination of factors, including content understanding and user interest in the author. This improves the accuracy of information recommendations, increases readership among authors' followers, and ultimately enhances the overall click-through rate of the recommendation system.

[0050] According to some embodiments, the fourth determining unit is configured to: determine a first sub-weight value based on the interest value in response to the interest value being greater than or equal to a threshold, wherein the first sub-weight value is greater than 1; or determine a second sub-weight value in response to the interest value being less than the threshold, wherein the second sub-weight value is equal to 1. It is understood that when it is determined that a user is interested in the author, the fourth determining unit uses the first sub-weight value as a weight value for calculating the first score, and performs a weighted calculation on the second score of the information published by the author with a weight value greater than 1, to obtain a larger first score, making the information more likely to be recommended to the user. However, when it is determined that the user is not interested in the author, the fourth determining unit uses the second sub-weight value, which has a value of 1, as a weight value for calculating the first score, and determines whether to recommend the information solely based on its content.

[0051] According to another aspect of this disclosure, a training apparatus for a machine learning model for recommendation information is provided. For example... Figure 4As shown, the training device 400 for the machine learning model includes: a third acquisition module 401, configured to acquire sample behavior data of sample users, wherein the sample behavior data includes the sample users' click behavior data on information within the platform and the sample users' follow list, and wherein the follow list includes authors followed by the sample users; a labeling module 402, configured to label the sample users' true interest values, wherein the true interest values ​​characterize the sample users' degree of interest in the authors; a fourth acquisition module 403, configured to acquire the domain information of the authors; a fifth acquisition module 404, configured to input the sample behavior data and the domain information of the authors into the machine learning model to acquire the sample users' predicted interest values, wherein the predicted interest values ​​indicate the sample users' predicted degree of interest in the authors; a calculation module 405, configured to calculate a loss value based on the true interest values ​​and the predicted interest values; and an adjustment module 406, configured to adjust the parameters of the machine learning model based on the loss values.

[0052] The operation of modules 401-406 of the machine learning model training device 400 is similar to the operation of steps S201-S206 described above, and will not be repeated here.

[0053] Therefore, the machine learning model training device 400 trains the machine learning model by constructing training samples such as sample user behavior data and author domain information, labeling the user's true interest value, and adjusting the model parameters based on the labeled true interest value and the model's output interest prediction value. This allows the model to predict the degree of user interest in an author. This prediction result can be used to calculate a ranking score for the information published by the author to determine whether to recommend this information to the user. By introducing machine learning technology to predict the degree of user interest in followed authors, the efficiency and accuracy of subsequent information recommendation can be improved.

[0054] According to embodiments of this disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0055] refer to Figure 5The present invention describes a structural block diagram of an electronic device 500 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0056] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0057] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, output unit 507, storage unit 508, and communication unit 509. Input unit 506 can be any type of device capable of inputting information to electronic device 500. Input unit 506 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device, and may include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 507 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 508 may include, but is not limited to, disk and optical disk. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth. TM Devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices and / or the like.

[0058] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as information recommendation methods. For example, in some embodiments, the information recommendation method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the information recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform any of the methods described above by any other suitable means (e.g., by means of firmware).

[0059] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0060] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0061] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0062] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0063] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0064] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0065] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0066] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as the technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A method for recommending information, comprising: obtaining behavior data of a user in a platform, wherein the behavior data comprises first click behavior data of the user on information in the platform and a following list of the user, and wherein the following list comprises authors followed by the user; obtaining, from the first click behavior data, second click behavior data of the user on information published by the authors, and determining a first association value between the user and the authors based on the first click behavior data and the second click behavior data, comprising: determining, based on the first click behavior data, a first click rate of the user on information in the platform in a first time period and a second click rate of the user on information in the platform in a second time period, wherein the first time period is longer than the second time period; determining, based on the second click behavior data, a third click rate of the user on information published by the authors in a first time period and a fourth click rate of the user on information published by the authors in a second time period; and determining the first association value based on a ratio of the third click rate to the first click rate and a ratio of the fourth click rate to the second click rate; determining an interest field of the user based on the behavior data; determining a second association value between the user and the authors based on a matching degree between the interest field and a field in which the authors are located; determining an interest value of the user on the authors based on the first association value and the second association value; and determining whether to recommend information published by the authors to the user based on the interest value.

2. The method of claim 1, wherein, Determining whether to recommend information published by the authors to the user based on the interest value comprises: determining a first score of information published by the authors based on the interest value, wherein the first score is used to indicate whether to recommend information published by the authors to the user.

3. The method of claim 2, wherein, Determining a first score of information published by the authors based on the interest value comprises: obtaining a second score of information published by the authors, wherein the second score is obtained based on a matching degree between content of information published by the authors and the behavior data of the user; determining a weight value used for weighted calculation of the second score based on the interest value to determine the first score.

4. The method of claim 3, wherein, Determining the weight value based on the interest value comprises: in response to the interest value being greater than or equal to a threshold value, determining a first sub-weight value based on the interest value, wherein the first sub-weight value is greater than 1; or in response to the interest value being less than the threshold value, determining a second sub-weight value, wherein the second sub-weight value is equal to 1.

5. An apparatus for recommending information, comprising: a first obtaining module configured to obtain behavior data of a user in a platform, wherein the behavior data comprises first click behavior data of the user on information in the platform and a following list of the user, and wherein the following list comprises authors followed by the user; The second obtaining module is configured to obtain second click behavior data of the user for information published by the author from the first click behavior data, and determine a first association value between the user and the author based on the first click behavior data and the second click behavior data, wherein the second obtaining module comprises: The first determining unit is configured to determine a first click rate of the user for information in a platform in a first time period and a second click rate of the user for information in the platform in a second time period based on the first click behavior data, wherein the first time period is longer than the second time period; The second determining unit is configured to determine a third click rate of the user for information published by the author in the first time period and a fourth click rate of the user for information published by the author in the second time period based on the second click behavior data; and The third determining unit is configured to determine the first association value based on a ratio of the third click rate to the first click rate and a ratio of the fourth click rate to the second click rate; The first determining module is configured to determine an interest field of the user based on the behavior data; The second determining module is configured to determine a second association value between the user and the author based on a matching degree of the interest field and a field in which the author is located; The third determining module is configured to determine an interest value of the user for the author based on the first association value and the second association value; and The fourth determining module is configured to determine whether to recommend information published by the author to the user based on the interest value.

6. The apparatus of claim 5, wherein, The fourth determining module is further configured to: determine a first score of the information published by the author based on the interest value, wherein the first score is used to indicate whether to recommend the information published by the author to the user.

7. The apparatus of claim 6, wherein, The fourth determining module comprises: an obtaining unit configured to obtain a second score of the information published by the author, wherein the second score is obtained based on a matching degree of content of the information published by the author and the behavior data of the user; a fourth determining unit configured to determine a weight value used for weighted calculation of the second score based on the interest value, to determine the first score.

8. The apparatus of claim 7, wherein the fourth determining unit is configured to: in response to the interest value being greater than or equal to a threshold value, determining a first sub-weight value based on the interest value, wherein the first sub-weight value is greater than 1; or determine a second sub-weight value in response to the interest value being less than the threshold value, wherein the second sub-weight value is equal to 1.

9. An electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

10. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-4.

11. A computer program product comprising a computer program, wherein, The computer program, when executed by a processor, implements the method of any one of claims 1-4.

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