Method, apparatus, computer-readable medium, and electronic device for recommending electronic resources

Abstract

Embodiments of the present application provide a method, an apparatus, a computer-readable medium, and an electronic device for recommending electronic resources. The method for recommending electronic resources includes: determining, based on the identification information of the self-selected resources collected in the user account, the set to which the self-selected resources belong from the preset sets respectively corresponding to each resource type; determining, by means of logistic regression, the popularity parameter corresponding to the self-selected resources based on the trigger information generated when the user views the self-selected resources; determining, based on the popularity parameters corresponding to the self-selected resources included in the set, the proportion of the user's attention to the set among all sets as the preference parameter indicating the degree of the user's attention to the set; and pushing recommendation information of electronic resources to the user account based on the preference parameters corresponding to each set. The technical solution of the embodiments of the present application improves the accuracy and comprehensiveness of user preference analysis and provides a good data basis for subsequent resource recommendation and combined utilization.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a method, an apparatus, a computer-readable medium, and an electronic device for recommending electronic resources. Background Art

[0002] During the operation of some systems, it is necessary to push relevant products, electronic resources, etc. to users. In related technologies, some operations of users when using the system are collected, and based on these operations, the preferences of users are judged. For example, by detecting some content clicked by the user, relevant information or news corresponding to this content, or other information of the same type as this content, is pushed.

[0003] The above method for processing user information is too single. It not only cannot fully determine user preferences, but may also result in incorrect analysis conclusions due to some random clicks or viewing behaviors of users, and then recommend inappropriate content to users. Therefore, the above method is easily misled by user usage information to generate incorrect push information, with low robustness, resulting in a large difference between the pushed content and user preferences, and causing inaccurate resource recommendation problems. Summary of the Invention

[0004] Embodiments of the present application provide a method, an apparatus, a computer-readable medium, and an electronic device for recommending electronic resources, which can at least improve the accuracy and comprehensiveness of user preference analysis to a certain extent, and provide a good data basis for subsequent resource recommendation and combined utilization.

[0005] Other features and advantages of the present application will become apparent through the following detailed description, or be learned in part through the practice of the present application.

[0006] According to one aspect of the embodiments of the present application, a method for recommending electronic resources is provided, including: determining, from preset sets respectively corresponding to each resource type, the set to which the self-selected resources belong based on the identification information of the self-selected resources collected in the user account; determining the heat parameter corresponding to the self-selected resources by means of logistic regression based on the trigger information generated when the user views the self-selected resources; determining the proportion of the attention degree of the user to the set among all sets based on the heat parameters corresponding to the self-selected resources included in the set as a preference parameter representing the attention degree of the user to the set; and pushing recommendation information of electronic resources to the user account based on the preference parameters corresponding to each set.

[0007] According to one aspect of the embodiments of the present application, there is provided an apparatus for recommending electronic resources, including: a collection unit configured to determine, from preset collections respectively corresponding to various resource types, the collection to which the self-selected resources belong based on the identification information of the self-selected resources collected in the user account; a first parameter unit configured to determine, by means of logistic regression, a popularity parameter corresponding to the self-selected resources based on the trigger information generated when the user views the self-selected resources; a second parameter unit configured to determine, based on the popularity parameters corresponding to the self-selected resources included in the collection, the proportion of the user's attention to the collection among all collections as a preference parameter representing the degree of the user's attention to the collection; and a push unit configured to push recommendation information of electronic resources to the user account based on the preference parameters corresponding to each collection.

[0008] In some embodiments of the present application, based on the foregoing solution, the second parameter unit includes: a first determination unit configured to determine the self-selected resources included in the collection based on the collection to which the self-selected resources belong; a second determination unit configured to calculate, with the popularity parameters corresponding to the self-selected resources included in the collection as weights, the resource proportion of the self-selected resources included in the collection among all collections, and use the resource proportion as the preference parameter corresponding to the collection.

[0009] In some embodiments of the present application, based on the foregoing solution, the second determination unit is configured to: calculate the preference weighted sum of the self-selected resources included in the collection with the popularity parameters corresponding to the self-selected resources included in the collection as weights; calculate the total resource preference in all collections based on the popularity parameters corresponding to all self-selected resources; and calculate the ratio between the preference weighted sum and the total resource preference as the preference parameter corresponding to the collection.

[0010] In some embodiments of the present application, based on the foregoing solution, the second determination unit is configured to: obtain a resource score corresponding to the self-selected resources included in the collection, where the resource score is used to represent the recommended value corresponding to the self-selected resources; calculate, with the popularity parameters and resource scores corresponding to the self-selected resources as weights, the resource proportion of the self-selected resources included in the collection among all collections, and use the resource proportion as the preference parameter corresponding to the collection.

[0011] In some embodiments of the present application, based on the foregoing solution, the trigger information includes the number of triggers within a set time period; the first parameter unit includes: a calculation unit configured to calculate, based on the number of triggers when the user views the self-selected resources, the value corresponding to the logistic regression function with the number of triggers as the power exponent as the popularity parameter corresponding to the self-selected resources.

[0012] In some embodiments of the present application, based on the foregoing solution, the calculation unit is configured to: calculate a second parameter with the trigger count when the user views the selected resource as the power exponent and the first parameter as the base; calculate the product between the second parameter and a third parameter to obtain a fourth parameter; calculate the sum between the fourth parameter and a fifth parameter to obtain a sixth parameter; and use the reciprocal of the sixth parameter as the popularity parameter corresponding to the selected resource.

[0013] In some embodiments of the present application, based on the foregoing solution, the trigger information includes the viewing duration and trigger count when the user views the selected resource within a set time period; the first parameter unit is configured to: perform normalization processing on the viewing duration based on the viewing duration when the user views the selected resource to generate a duration parameter; and calculate the value corresponding to the logistic regression function with the trigger count as the power exponent based on the duration parameter and the trigger count, and use it as the popularity parameter corresponding to the selected resource.

[0014] In some embodiments of the present application, based on the foregoing solution, the set unit is used to: determine the resource type corresponding to the selected resource based on the identification information of the selected resources collected in the user account; and determine the set to which the selected resource belongs from the preset sets corresponding to each resource type.

[0015] In some embodiments of the present application, based on the foregoing solution, the push unit includes: a target set unit, configured to determine the target set preferred by the user based on the preference parameters corresponding to each set and a set parameter threshold; and an information push unit, configured to push the electronic resource information in the target set to the user account.

[0016] In some embodiments of the present application, based on the foregoing solution, the information push unit is configured to: detect target electronic resources belonging to the target set from the electronic resource library; analyze the profit and loss status of the target electronic resources based on the information of the target electronic resources; and push the target electronic resources corresponding to when the profit and loss status meets the preset conditions to the terminal corresponding to the user account.

[0017] In some embodiments of the present application, based on the foregoing solution, the device for recommending electronic resources is further used to: when detecting the update information of the selected resources historically collected by the user, determine the current corresponding new selected resources based on the update information; and update the preference parameters of the user for each set based on the set corresponding to the new selected resources and the trigger information.

[0018] In some embodiments of the present application, based on the foregoing solution, the pushing unit is configured to: generate recommendation information of electronic resources based on the preference parameters corresponding to each of the sets; and send the recommendation information to the terminal corresponding to the user account, so that the terminal displays the recommendation information in the interface list corresponding to the self-selected resources collected by the user.

[0019] According to one aspect of the embodiments of the present application, there is provided a computer-readable medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for recommending electronic resources as described in the above embodiments is implemented.

[0020] According to one aspect of the embodiments of the present application, there is provided an electronic device, including: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method for recommending electronic resources as described in the above embodiments.

[0021] According to one aspect of the embodiments of the present application, there is provided a computer program product or a computer program, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for recommending electronic resources provided in the above various alternative implementations.

[0022] In the technical solution provided by some embodiments of the present application, the self-selected resources of the user are used as preferences, and then the preferences are adjusted based on the trigger information of the user, and then the resource preference information of the user is comprehensively determined. By combining the self-selected resource information and the trigger information of the user to determine the preference situation of the user, and finally recommending corresponding electronic resources to the user based on the preference situation, the accuracy and comprehensiveness of user preference analysis are improved, providing a good data basis for subsequent resource recommendation and combined utilization.

[0023] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings here are incorporated into the specification and constitute a part of this specification, showing the embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts. In the drawings:

[0025] Figure 1A schematic diagram showing an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied;

[0026] Figure 2 A schematic diagram schematically showing an exemplary system architecture according to an embodiment of the present application;

[0027] Figure 3 A flowchart schematically showing a method for recommending electronic resources according to an embodiment of the present application;

[0028] Figure 4 A schematic diagram of an interface for self - selected resources schematically showing according to an embodiment of the present application;

[0029] Figure 5 A schematic diagram schematically showing the generation of recommendation information according to an embodiment of the present application;

[0030] Figure 6 A schematic diagram schematically showing the determination of a set according to an embodiment of the present application;

[0031] Figure 7 A schematic diagram schematically showing the determination of a set in a stock application environment according to an embodiment of the present application;

[0032] Figure 8 A flowchart schematically showing the calculation of a popularity parameter according to an embodiment of the present application;

[0033] Figure 9 A schematic diagram showing the relationship between a weight multiple and the number of clicks according to an embodiment of the present application;

[0034] Figure 10 A flowchart schematically showing the calculation of a preference parameter according to an embodiment of the present application;

[0035] Figure 11 A flowchart schematically showing the calculation of a preference parameter based on a recommendation value according to an embodiment of the present application;

[0036] Figure 12 A flowchart schematically showing the pushing of electronic resource information in a target set to a user account according to an embodiment of the present application;

[0037] Figure 13 A schematic diagram schematically showing the recommendation of electronic resources based on a profit - and - loss status according to an embodiment of the present application;

[0038] Figure 14 A flowchart schematically showing the pushing of recommendation information of electronic resources to a user account according to an embodiment of the present application;

[0039] Figure 15A schematic diagram of a terminal interface showing display preference information according to an embodiment of the present application is shown;

[0040] Figure 16 A flowchart of updating recommended resources according to an embodiment of the present application is schematically shown;

[0041] Figure 17 A block diagram of a device for recommending electronic resources according to an embodiment of the present application is schematically shown;

[0042] Figure 18 A schematic diagram of the structure of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown. Detailed implementation manners

[0043] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0044] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application.

[0045] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0046] The flowcharts shown in the drawings are only illustrative and do not necessarily include all the content and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can be decomposed, while some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0047] Artificial Intelligence (AI) uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence. It is a theory, method, technology, and application system that can perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a way similar to human intelligence. Artificial intelligence also studies the design principles and implementation methods of various intelligent machines, enabling machines to have the functions of perception, reasoning, and decision-making.

[0048] Artificial intelligence technology is an interdisciplinary subject with a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0049] Machine Learning (ML) is an interdisciplinary subject that involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve their own performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications cover all fields of artificial intelligence. Machine learning and deep learning usually include technologies such as artificial neural networks, belief networks, reinforcement learning, transfer learning, and inductive learning.

[0050] With the research and progress of artificial intelligence technology, artificial intelligence technology has been studied and applied in multiple fields. For example, common ones include smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, driverless, autonomous driving, drones, robots, smart healthcare, smart customer service, etc. It is believed that with the development of technology, artificial intelligence technology will be applied in more fields and play an increasingly important role.

[0051] The solution provided in the embodiments of this application involves technologies such as machine learning and data analysis in artificial intelligence, and will be specifically described through the following embodiments:

[0052] Figure 1 The figure shows a schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of this application can be applied.

[0053] As Figure 1 shown, the system architecture may include terminal devices (such as Figure 1One or more of the smart phone 101, tablet computer 102, and portable computer 103 shown (which can of course also be a desktop computer, etc.), network 104, and server 105. The network 104 serves as a medium for providing a communication link between the terminal device and the server 105. The network 104 can include various connection types, such as wired communication links, wireless communication links, and so on.

[0054] It should be understood that Figure 1 the number of terminal devices, networks, and servers in is merely illustrative. According to implementation requirements, there can be any number of terminal devices, networks, and servers. For example, the server 105 can be a server cluster composed of multiple servers, etc.

[0055] Users can use the terminal device to interact with the server 105 via the network 104 to receive or send messages, etc. The server 105 can be a server that provides various services. For example, the user uses the terminal device 103 (which can also be the terminal device 101 or 102) to upload the identification information of the selected resources collected in the user account to the server 105 to determine the set to which the selected resources belong; based on the trigger information generated when the user views the selected resources, determine the popularity parameter corresponding to the selected resources by means of logistic regression; based on the selected resources included in the set and the popularity parameter corresponding to the selected resources, determine the preference parameter indicating the degree of the user's attention to the set; based on the preference parameters corresponding to each set, push the recommendation information of the electronic resources to the user account.

[0056] In the above solution, based on the identification information of the selected resources collected in the user account, the set to which the selected resources belong is determined. Then, based on the trigger information generated when the user views the selected resources, the popularity parameter corresponding to the selected resources is determined, so as to determine the preference parameter indicating the degree of the user's attention to the set based on the selected resources included in the set and their corresponding popularity parameters. Based on the preference parameters corresponding to the set, the recommendation information of the electronic resources is pushed to the user account. In this solution, the user's selected resources are used as preferences, and then the preferences are adjusted based on the user's trigger information. Furthermore, the user's resource preference information is comprehensively determined. By combining the user's selected resource information and trigger information, the user's preference situation is determined. Finally, based on the preference situation, the corresponding electronic resources are recommended to the user, improving the accuracy and comprehensiveness of the user preference analysis and providing a good data basis for subsequent resource recommendation and combined utilization.

[0057] It should be noted that the method for recommending electronic resources provided in the embodiments of the present application is generally executed by the server 105. Correspondingly, the device for recommending electronic resources is generally set in the server 105. However, in other embodiments of the present application, the terminal device can also have a similar function as the server, so as to execute the solution for recommending electronic resources provided in the embodiments of the present application.

[0058] As Figure 2 shown, in an embodiment of the present application, the electronic resources in this embodiment may be electronic resources such as stocks, securities, or funds. Therefore, when obtaining electronic resource information, in this embodiment, the server 105 may be used to directly obtain the securities trading information generated in the trading server 106, so as to push the corresponding electronic resource information to the user terminal based on these securities trading information. For example, push stock information, fund information, etc. to Figure 2 the smart phone 101, tablet computer 102, or portable computer 103 in

[0059] The implementation details of the technical solution of the embodiment of the present application are elaborated in detail below:

[0060] Figure 3 shows a flowchart of a method for recommending electronic resources according to an embodiment of the present application. The method for recommending electronic resources may be executed by a server, and the server may be the Figure 1 server shown in Figure 3 shown. Referring to Figure 3 shown, the method for recommending electronic resources includes at least steps S310 to S340, which are introduced in detail as follows:

[0061] In step S310, based on the identification information of the self-selected resources collected in the user account, determine the set to which the self-selected resources belong from the preset sets corresponding to each resource type.

[0062] In an embodiment of the present application, in actual application, the user will mark or collect some electronic resources that they are interested in for future viewing or management. In this case, a corresponding self-selected resource list will be generated and displayed on the interface of the terminal, as Figure 4 shown.

[0063] Specifically, the identification information of the self-selected resources in this embodiment may be the name, number, etc. of the self-selected resources.

[0064] In this embodiment, first obtain the information of the self-selected resources collected in the user account, so as to obtain the set of self-selected resources based on the identification information of the self-selected resources. When determining the set to which the self-selected resources belong based on the identification information of the self-selected resources in this embodiment, first obtain the preset sets corresponding to each resource type, that is, the industry information, resource type, resource names included in each industry information, etc. in each set, and then determine the set corresponding to the self-selected resources from the preset sets corresponding to each resource type.

[0065] Exemplarily, as Figure 4As shown, the electronic resources in this embodiment can be stocks. When a user uses a stock software, they will add the stocks they are interested in to the corresponding list and use them as the stocks in the self-selected list. Each self-selected resource has its corresponding sector. Specifically, these sectors can be the consumer sector, the growth sector, the cyclical sector, the financial sector, the stable sector, and so on.

[0066] In step S320, based on the trigger information generated when the user views the self-selected resources, the heat parameter corresponding to the self-selected resources is determined by means of logistic regression.

[0067] As Figure 5 shown, in an embodiment of the present application, the user will generate corresponding trigger information when viewing the self-selected resources within each period of time. For example, the number of clicks within each period of time, the duration of each view, etc. In this embodiment, the heat parameter corresponding to the self-selected resources is determined through the above trigger information.

[0068] Specifically, in this embodiment, the method of calculating the heat parameter can be based on the number of clicks and the viewing duration, and the weighted sum between the trigger information is calculated by means of logistic regression as the heat parameter corresponding to the self-selected resources, so as to measure the user stickiness of the user to each electronic resource through the heat parameter.

[0069] In step S330, based on the heat parameters corresponding to the self-selected resources included in the set, the proportion of the user's attention to the set among all sets is determined as the preference parameter indicating the degree of the user's attention to the set.

[0070] As Figure 5 shown, in an embodiment of the present application, after determining the heat parameter corresponding to the self-selected resources, in this embodiment, based on the self-selected resources included in the set and the heat parameters corresponding to the self-selected resources, the preference parameter of the user's attention degree to the set is determined, so as to measure the preference degree of the user to each set through the preference parameter.

[0071] Specifically, the self-selected resources in this embodiment include the collection status of the electronic resources in each set. The heat parameter represents the user stickiness to each stock. Based on the situation of the self-selected resources, by aggregating the heat parameters of the user for each self-selected resource, the proportion of the user's attention to the set among all sets can be determined through weighted calculation as the attention degree of the user to each set, that is, the preference parameter.

[0072] In step S340, based on the preference parameters corresponding to each set, the recommended information of the electronic resources is pushed to the user account.

[0073] As Figure 5As shown, in one embodiment of the present application, after calculating the corresponding preference parameters of each set, the set corresponding to the preference parameter greater than the set threshold is determined as the target set. The electronic resources in the target set are pushed to the user as recommended information.

[0074] In one embodiment of the present application, the process of step S310 for determining the set to which the self-selected resources belong from the preset sets corresponding to each resource type based on the identification information of the self-selected resources collected in the user account specifically includes: determining the resource type corresponding to the self-selected resources based on the identification information of the self-selected resources collected in the user account; and determining the set to which the self-selected resources belong based on the set to which the resource type belongs.

[0075] As Figure 6 shown, in one embodiment of the present application, first, based on the identification information of the self-selected resources collected in the user account, that is, electronic resources 1 to electronic resources N, the resource types corresponding to the self-selected resources are determined, that is, resource types 1 to resource types M; then, based on the sets of each resource type, the sets of the self-selected resources are determined, that is, the first set to the fifth set.

[0076] As Figure 7 shown, in the application scenario of a stock software, assume that the user adds a total of N stocks, namely stocks 1 to stocks N, into the self-selection, which are S(1), S(2), ……, S(N) respectively; the above N stocks correspond to 28 first-level industries of Shenwan. Excluding the "Comprehensive" industry, there are 27 remaining first-level industries, and each stock belongs to and only belongs to one of these industries, such as Figure 7 the industries 1 to 27 in. The above 27 first-level industries can be classified into 5 major sectors according to their attributes: consumer sector, growth sector, cycle sector, financial sector, and stable sector. The specific sector-industry affiliations are as follows: Consumer sector: Food and Beverage, Home Appliances, Agriculture, Forestry, Animal Husbandry and Fishery, Textile and Apparel, Light Industry and Manufacturing, Commercial Trade, Leisure Services, Medicine and Biology; Growth sector: Electronics, Computer, Electrical Equipment, Media, Military Industry, Telecommunications; Cycle sector: Chemical Industry, Automobile, Machinery, Nonferrous Metals, Building Materials, Mining, Steel; Financial sector: Banking, Non-bank Finance, Real Estate; Stable sector: Utilities, Architectural Decoration, Transportation. Thus, each stock of this user can correspond to one of the 5 major sectors, obtaining the final sector corresponding to the stock.

[0077] In one embodiment of the present application, the trigger information in step S320 includes the number of triggers within a set time period. The process of determining the popularity parameter corresponding to the self-selected resources by means of logistic regression based on the trigger information generated when the user views the self-selected resources specifically includes: calculating the value corresponding to the logistic regression function with the number of triggers as the power exponent based on the number of triggers when the user views the self-selected resources, and using it as the popularity parameter corresponding to the self-selected resources.

[0078] In an embodiment of the present application, the trigger information includes the number of triggers within a set time period. In this embodiment, after obtaining the number of triggers, based on this number of triggers, the heat parameter corresponding to the self-selected resource can be obtained through calculation. Specifically, the calculation method can be to calculate the data corresponding to the logistic regression function with the number of triggers as the power exponent as the heat parameter.

[0079] In an embodiment of the present application, as Figure 8 shown, the process of calculating the value corresponding to the logistic regression function with the number of triggers as the power exponent as the heat parameter corresponding to the self-selected resource based on the number of triggers when the user views the self-selected resource specifically includes steps S3210 to S3240, which are described in detail as follows:

[0080] Step S3210: Calculate the second parameter with the number of triggers when the user views the self-selected resource as the power exponent and the first parameter as the base.

[0081] Step S3220: Calculate the product between the second parameter and the third parameter to obtain the fourth parameter.

[0082] Step S3230: Calculate the sum between the fourth parameter and the fifth parameter to obtain the sixth parameter.

[0083] Step S3240: Take the reciprocal of the sixth parameter as the heat parameter corresponding to the self-selected resource.

[0084] In an embodiment of the present application, when considering the click data in the recent 7 days, the preference judgment of the user can be corrected and optimized by analyzing the user's latest behavior habits. In this embodiment, through the logistic regression function, that is, the S function, calculate the corresponding value with the number of triggers as the power exponent as the heat parameter corresponding to the self-selected resource, that is, the weight multiple, so as to correct the weight of each click on the self-selected individual stock through this weight multiple. The specific logistic regression function is as follows:

[0085]

[0086] Among them, optionally, parameter a = 0.221512; b = 0.778488; e = 2.71828.

[0087] The relationship between the weight multiple and the number of clicks in this embodiment is as Figure 9 shown. The advantage of using the S function is that it well fits the user's behavior. At the same time, it also has a certain attenuation effect, which better represents the user's true preference. Specifically speaking, clicking on a stock recently represents the user's attention to the stock, but after the number of clicks exceeds a certain amount, the marginal utility will decrease.

[0088] In an embodiment of the present application, the trigger information includes the viewing duration and the number of triggers when the user views the selected resources within a set time period; the trigger information in step S320 includes the number of triggers within a set time period. The process of determining the popularity parameter corresponding to the selected resources based on the trigger information generated when the user views the selected resources through logistic regression may further include:

[0089] Based on the viewing duration of the user viewing the selected resources, perform normalization processing on the viewing duration to generate a duration parameter;

[0090] Based on the duration parameter and the number of triggers, calculate the value corresponding to the logistic regression function with the number of triggers as the power exponent as the popularity parameter corresponding to the selected resources.

[0091] In an embodiment of the present application, in this embodiment, the viewing duration can also be used as one of the trigger information. By combining the viewing duration and the number of triggers, calculate and determine the popularity parameter corresponding to the selected resources. The specific calculation method is to perform normalization processing on the viewing duration of each selected resource within a set time period to generate a duration parameter, then calculate the value corresponding to the logistic regression function with the number of triggers as the power exponent, and perform weighted processing on this data through the viewing duration to obtain the popularity parameter corresponding to the selected resources.

[0092] By the above method of combining the viewing duration to measure the popularity parameter of the selected resources, the comprehensiveness and accuracy of measuring the user stickiness of the user to the electronic resources can be improved, and thus a more objective set preference parameter can be obtained.

[0093] In an embodiment of the present application, in step S330, based on the popularity parameter corresponding to the selected resources included in the set, determine the proportion of the user's attention to the set in all sets as the preference parameter indicating the degree of the user's attention to the set, including steps S331 to S332, which are described in detail as follows:

[0094] In step S331, based on the set to which the selected resources belong, determine the selected resources included in the set;

[0095] In step S332, using the popularity parameter corresponding to the selected resources included in the set as the weight, calculate the resource proportion of the selected resources included in the set in all sets, and use the resource proportion as the preference parameter corresponding to the set.

[0096] In one embodiment of the present application, first, based on the set of each selected resource, determine the selected resources included in each set. Then, using the popularity parameter corresponding to the selected resources included in the set as the weight, calculate the resource proportion of the selected resources included in the set among all sets, and use the resource proportion as the preference parameter corresponding to the set. Specifically, the calculation method can be to calculate the total preference parameter of the selected resources included in a set with the popularity parameter as the weight, and then divide the total preference parameter by the sum of the total preference parameters corresponding to all sets to obtain the corresponding resource proportion as the preference parameter corresponding to the set.

[0097] In one embodiment of the present application, in step S332, the process of calculating the resource proportion of the selected resources included in the set among all sets with the popularity parameter corresponding to the selected resources included in the set as the weight, and using the resource proportion as the preference parameter corresponding to the set is as Figure 10 shown, and includes the following steps:

[0098] Step S3321, calculate the preference weighted sum of the selected resources included in the set with the popularity parameter corresponding to the selected resources included in the set as the weight;

[0099] Step S3322, calculate the total resource preference in all sets based on the popularity parameters corresponding to all selected resources;

[0100] Step S3323, calculate the ratio between the preference weighted sum and the total resource preference as the preference parameter corresponding to the set.

[0101] Specifically, when calculating the preference weighted sum, with the popularity parameter corresponding to the selected resources included in the set as the weight, add up the popularity parameters corresponding to all the selected resources in the set to obtain the preference weighted sum corresponding to a set, that is:

[0102] ∑ k [S(i)×ω]

[0103] where i is the identifier of each electronic resource, k is the identifier of the resource type in the set, and w is the modified weight multiple of the electronic resource.

[0104] Specifically, S(i) is used to represent that when the i-th electronic resource belongs to the set, S(i)=1, otherwise S(i)=0.

[0105] Secondly, calculate the total resource preference in all sets based on the popularity parameters corresponding to all selected resources, that is, add up the preference weighted sums corresponding to all sets to obtain:

[0106] ∑ n ∑ k [S(i)×ω]

[0107] Among them, n represents the set identifier.

[0108] Finally, calculate the ratio between the preference weighted sum and the total resource preference:

[0109]

[0110] In this embodiment, the ratio calculated by the above formula is used as the preference parameter corresponding to the set, so as to measure the user's preference degree for a set through the preference parameter.

[0111] In an embodiment of the present application, in the process of step S332, taking the heat parameter corresponding to the self-selected resources included in the set as the weight, calculating the resource proportion of the self-selected resources included in the set among all sets, and taking the resource proportion as the preference parameter corresponding to the set, the following steps may further be included, as Figure 11 shown:

[0112] Step S3324, obtain the resource score corresponding to the self-selected resources included in the set, where the resource score is used to represent the recommended value corresponding to the self-selected resources;

[0113] Step S3325, taking the heat parameter and the resource score corresponding to the self-selected resources as the weights, calculate the resource proportion of the self-selected resources included in the set among all sets, and take the resource proportion as the preference parameter corresponding to the set.

[0114] Specifically, the purpose of calculating the preference parameter in this embodiment is to recommend electronic resource information to the user based on the preference parameter. However, in actual applications, simply recommending electronic resources based on user preferences often cannot achieve the best recommendation purpose. Therefore, in this embodiment, first obtain the resource scores corresponding to the self-selected resources in each set to identify the recommended value corresponding to the self-selected resources. Then, based on the heat parameter and the resource score corresponding to the self-selected resources as the weights, calculate the resource proportion of the self-selected resources included in the set among all sets, and take the resource proportion as the preference parameter corresponding to the set.

[0115] In an embodiment of the present application, in the process of step S340, based on the preference parameters corresponding to each set, pushing electronic resource information to the user account specifically includes: determining the target set preferred by the user based on the preference parameters corresponding to each set and the set parameter threshold; pushing the electronic resource information in the target set to the user account.

[0116] In an embodiment of the present application, as Figure 12 shown, the process of pushing the electronic resource information in the target set to the user account specifically includes steps S121 to S123, and the details are as follows:

[0117] Step S121, detect the target electronic resources belonging to the target set from the electronic resource library;

[0118] Step S122: Analyze the profit and loss status of the target electronic resource based on the information of the target electronic resource;

[0119] Step S123: Push the target electronic resource corresponding to the profit and loss status meeting the preset conditions to the terminal corresponding to the user account.

[0120] As Figure 13 shown, in the application scenario of stocks, after determining the target set, detect the target electronic resources belonging to the target set from the electronic resource library. For example, the detected target electronic resources are Stock K to Stock T; then, based on the information of the target electronic resources, analyze their corresponding profit and loss status, and determine the target electronic resources corresponding to the preset conditions, that is, as the recommended electronic resources, and push these electronic resources to the terminal corresponding to the user account.

[0121] By the above method, it can ensure that the recommended self-selected resources are oriented to user preferences, and it can also ensure that when recommending electronic resources to users, the recommended electronic resources are of higher quality than other electronic resources, so as to improve the user utilization rate.

[0122] In an embodiment of the present application, as Figure 14 shown, in step S340, based on the preference parameters corresponding to each set, push the recommendation information of the electronic resources to the user account, including steps S341 to S342:

[0123] In step S341, generate the recommendation information of the electronic resources based on the preference parameters corresponding to each set;

[0124] In step S342, send the recommendation information to the terminal corresponding to the user account, so that the terminal displays the recommendation information in the interface list corresponding to the self-selected resources collected by the user.

[0125] As Figure 15 shown, after determining the preference parameters corresponding to each set, generate the recommendation information of the electronic resources based on the preference parameters. Among them, the recommendation information can be the specific name of the electronic resource or a sequence composed of the names of the electronic resources. Then, send the recommendation information to the user terminal to display the recommendation information in the interface list corresponding to the self-selected resources collected by the user. For example, Figure 15 the recommendation information displayed at the upper part of the self-selected resource list: Stock L, Stock O, Stock R.

[0126] In an embodiment of the present application, as Figure 16 shown, the above method further includes steps S161 to S163:

[0127] Step S161: When detecting the update information of the self-selected resources collected by the user in history, determine the current corresponding new self-selected resources based on the update information;

[0128] Step S162: Based on the set corresponding to the new self-selected resources and the trigger information, update the preference parameters of the user for each set;

[0129] Step S163: Select new recommended resources based on the preference parameters.

[0130] Specifically, in this embodiment, when detecting the update information of the self-selected resources collected by the user in history, that is, the user's preferences have changed, the current corresponding new self-selected resources are determined based on the update information; then, based on the set corresponding to the new self-selected resources and the trigger information, the preference parameters of the user for each set are updated; finally, new recommended resources are selected based on the preference parameters. Through the above self-selected resource information tracking method, the user's preferences can be tracked in real time, and updated resource information can be generated and recommended to the user terminal, thereby ensuring the timeliness of the recommended information.

[0131] The following introduces the device embodiments of the present application, which can be used to execute the method for recommending electronic resources in the above embodiments of the present application. It can be understood that the device can be a computer program (including program code) running in a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided by the embodiments of the present application. For the details not disclosed in the device embodiments of the present application, please refer to the embodiments of the method for recommending electronic resources in the above of the present application.

[0132] Figure 17 The block diagram of a device for recommending electronic resources according to an embodiment of the present application is shown.

[0133] Refer to Figure 17 As shown, a device 1700 for recommending electronic resources according to an embodiment of the present application includes: a set unit 1710, configured to determine the set to which the self-selected resources belong from the preset sets corresponding to each resource type based on the identification information of the self-selected resources collected in the user account; a first parameter unit 1720, configured to determine the popularity parameter corresponding to the self-selected resources by means of logistic regression based on the trigger information generated when the user views the self-selected resources; a second parameter unit 1730, configured to determine the proportion of the user's attention to the set in all sets based on the popularity parameter corresponding to the self-selected resources included in the set as the preference parameter representing the user's attention degree to the set; a push unit 1740, configured to push the recommended information of the electronic resources to the user account based on the preference parameters corresponding to each set.

[0134] In some embodiments of the present application, based on the foregoing solution, the second parameter unit 1730 includes: a first determination unit configured to determine the self-selected resources included in the set based on the set to which the self-selected resources belong; a second determination unit configured to calculate, with the heat parameters corresponding to the self-selected resources included in the set as weights, the resource proportion of the self-selected resources included in the set among all sets, and use the resource proportion as the preference parameter corresponding to the set.

[0135] In some embodiments of the present application, based on the foregoing solution, the second determination unit is configured to: calculate the preference weighted sum of the self-selected resources included in the set with the heat parameters corresponding to the self-selected resources included in the set as weights; calculate the total resource preference in all sets based on the heat parameters corresponding to all self-selected resources;

[0136] calculate the ratio between the preference weighted sum and the total resource preference as the preference parameter corresponding to the set.

[0137] In some embodiments of the present application, based on the foregoing solution, the second determination unit is configured to: obtain the resource score corresponding to the self-selected resources included in the set, where the resource score is used to represent the recommended value corresponding to the self-selected resources; calculate, with the heat parameters and resource scores corresponding to the self-selected resources as weights, the resource proportion of the self-selected resources included in the set among all sets, and use the resource proportion as the preference parameter corresponding to the set.

[0138] In some embodiments of the present application, based on the foregoing solution, the trigger information includes the number of triggers within a set time period; the first parameter unit includes: a calculation unit configured to calculate, based on the number of triggers when the user views the self-selected resources, the value corresponding to the logistic regression function with the number of triggers as the power exponent as the heat parameter corresponding to the self-selected resources.

[0139] In some embodiments of the present application, based on the foregoing solution, the calculation unit is configured to: calculate a second parameter with the number of triggers when the user views the self-selected resources as the power exponent and a first parameter as the base; calculate the product of the second parameter and a third parameter to obtain a fourth parameter; calculate the sum of the fourth parameter and a fifth parameter to obtain a sixth parameter; use the reciprocal of the sixth parameter as the heat parameter corresponding to the self-selected resources.

[0140] In some embodiments of the present application, based on the foregoing solution, the trigger information includes the viewing duration and the trigger count when the user views the selected resources within a set time period; the first parameter unit 1720 is configured to: perform normalization processing on the viewing duration based on the viewing duration when the user views the selected resources to generate a duration parameter; calculate the value corresponding to the logistic regression function with the trigger count as the power exponent based on the duration parameter and the trigger count, and use it as the popularity parameter corresponding to the selected resources.

[0141] In some embodiments of the present application, based on the foregoing solution, the set unit 1710 is configured to: determine the resource type corresponding to the selected resources based on the identification information of the selected resources collected in the user account; determine the set to which the selected resources belong from the preset sets corresponding to each resource type.

[0142] In some embodiments of the present application, based on the foregoing solution, the push unit 1740 includes: a target set unit, configured to determine the target set preferred by the user based on the preference parameters corresponding to each set and the set parameter threshold; an information push unit, configured to push the electronic resource information in the target set to the user account.

[0143] In some embodiments of the present application, based on the foregoing solution, the information push unit is configured to: detect the target electronic resources belonging to the target set from the electronic resource library; analyze the profit and loss status of the target electronic resources based on the information of the target electronic resources; push the target electronic resources corresponding to when the profit and loss status meets the preset conditions to the terminal corresponding to the user account.

[0144] In some embodiments of the present application, based on the foregoing solution, the device 1700 for recommending electronic resources is further configured to: when detecting the update information of the selected resources historically collected by the user, determine the current corresponding new selected resources based on the update information; update the preference parameters of the user for each set based on the set corresponding to the new selected resources and the trigger information.

[0145] In some embodiments of the present application, based on the foregoing solution, the push unit 1740 is configured to: generate recommendation information of electronic resources based on the preference parameters corresponding to each set; send the recommendation information to the terminal corresponding to the user account, so that the terminal displays the recommendation information in the interface list corresponding to the selected resources collected by the user.

[0146] Figure 18 The structural schematic diagram of the computer system of the electronic device suitable for implementing the embodiments of the present application is shown.

[0147] It should be noted that Figure 18The computer system 1800 of the illustrated electronic device is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present application.

[0148] As Figure 18 shown, the computer system 1800 includes a central processing unit (CPU) 1801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1802 or the program loaded from the storage section 1808 into the random access memory (RAM) 1803, such as executing the methods described in the above embodiments. In the RAM 1803, various programs and data required for system operation are also stored. The CPU 1801, ROM 1802, and RAM 1803 are connected to each other via a bus 1804. An input / output (I / O) interface 1805 is also connected to the bus 1804.

[0149] The following components are connected to the I / O interface 1805: an input section 1806 including a keyboard, a mouse, etc.; an output section 1807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and speakers, etc.; a storage section 1808 including a hard disk, etc.; and a communication section 1809 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1809 performs communication processing via a network such as the Internet. A drive 1810 is also connected to the I / O interface 1805 as required. A removable medium 1811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1810 as required so that a computer program read from it can be installed into the storage section 1808 as required.

[0150] Specifically, according to the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments of the present application include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program 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 section 1809 and / or installed from the removable medium 1811. When the computer program is executed by the central processing unit (CPU) 1801, various functions defined in the system of the present application are executed.

[0151] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present application, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable computer program is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0152] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0153] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the units themselves.

[0154] According to one aspect of the present application, there is provided a computer program product or a computer program, the computer program product or the computer program including computer instructions, and the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above various alternative implementation manners.

[0155] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist alone without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device implements the methods described in the above embodiments.

[0156] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0157] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented in software or in a manner of software combined with necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which may be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the methods according to the embodiments of the present application.

[0158] After considering the specification and practicing the disclosed embodiments herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0159] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for recommending electronic resources, characterized in that, it includes: Based on the identification information of the self-selected resources collected in the user account, determine the set to which the self-selected resources belong from the preset sets corresponding to each resource type; Based on the trigger information generated when the user views the self-selected resources, determine the popularity parameter corresponding to the self-selected resources by means of logistic regression; Obtain the resource scores corresponding to the self-selected resources included in the set, where the resource scores are used to represent the recommended value corresponding to the self-selected resources; Using the popularity parameter and the resource score corresponding to the self-selected resources as weights, calculate the resource proportion of the self-selected resources included in the set among all sets, and use the resource proportion as the preference parameter corresponding to the set; Based on the preference parameters corresponding to each set, push recommendation information of electronic resources to the user account.

2. The method according to claim 1, characterized in that, the trigger information includes the number of triggers within a set time period; Based on the trigger information generated when the user views the self-selected resources, determining the popularity parameter corresponding to the self-selected resources by means of logistic regression includes: Based on the number of triggers when the user views the self-selected resources, calculate the value corresponding to the logistic regression function with the number of triggers as the power exponent, as the popularity parameter corresponding to the self-selected resources.

3. The method according to claim 2, characterized in that, Based on the number of triggers when the user views the self-selected resources, calculating the value corresponding to the logistic regression function with the number of triggers as the power exponent, as the popularity parameter corresponding to the self-selected resources, includes: Taking the number of triggers when the user views the self-selected resources as the power exponent and the first parameter as the base, calculate to obtain the second parameter; Calculate the product between the second parameter and the third parameter to obtain the fourth parameter; Calculate the sum between the fourth parameter and the fifth parameter to obtain the sixth parameter; Take the reciprocal of the sixth parameter as the popularity parameter corresponding to the self-selected resources.

4. The method according to claim 1, characterized in that, the trigger information includes the viewing duration and the number of triggers when the user views the self-selected resources within a set time period; Based on the trigger information generated when the user views the self-selected resources, determining the popularity parameter corresponding to the self-selected resources by means of logistic regression includes: Based on the viewing duration when the user views the self-selected resources, perform normalization processing on the viewing duration to generate a duration parameter; Based on the duration parameter and the number of triggers, calculate the value corresponding to the logistic regression function with the number of triggers as the power exponent, as the popularity parameter corresponding to the self-selected resources.

5. The method according to claim 1, characterized in that, Based on the identification information of the self-selected resources collected in the user account, determining the set to which the self-selected resources belong from the preset sets corresponding to each resource type includes: Based on the identification information of the self-selected resources collected in the user account, determine the resource type corresponding to the self-selected resources; Determine the set to which the self-selected resources belong from the preset sets corresponding to each resource type.

6. The method according to claim 1, characterized in that, Based on the preference parameters corresponding to each of the sets, push recommendation information of electronic resources to the user account, including: Based on the preference parameters corresponding to each of the sets and a set parameter threshold, determine a target set preferred by the user; Push the electronic resource information in the target set to the user account.

7. The method according to claim 6, wherein, Pushing the electronic resource information in the target set to the user account includes: Detect target electronic resources belonging to the target set from an electronic resource library; Based on the information of the target electronic resources, analyze the profit and loss status of the target electronic resources; Push the target electronic resources corresponding to when the profit and loss status meets a preset condition to a terminal corresponding to the user account.

8. The method according to claim 1, wherein, The method further includes: When detecting update information of self-selected resources collected by a user, determine current corresponding new self-selected resources based on the update information; Based on the sets corresponding to the new self-selected resources and trigger information, update the preference parameters of the user for each set.

9. The method according to claim 1, wherein, Based on the preference parameters corresponding to each of the sets, push recommendation information of electronic resources to the user account, including: Generate recommendation information of electronic resources based on the preference parameters corresponding to each of the sets; Send the recommendation information to a terminal corresponding to the user account, so that the terminal displays the recommendation information in an interface list corresponding to self-selected resources collected by the user.

10. An apparatus for recommending electronic resources, wherein, It includes: A set unit, configured to determine a set to which the self-selected resources belong from preset sets respectively corresponding to each resource type based on identification information of self-selected resources collected in a user account; A first parameter unit, configured to determine a popularity parameter corresponding to the self-selected resources by means of logistic regression based on trigger information generated when the user views the self-selected resources; A second parameter unit, configured to obtain a resource score corresponding to the self-selected resources included in the set, where the resource score is used to represent the recommended value corresponding to the self-selected resources; calculate the resource proportion of the self-selected resources included in the set in all sets with the popularity parameter and the resource score corresponding to the self-selected resources as weights, and use the resource proportion as the preference parameter corresponding to the set; A push unit, configured to push recommendation information of electronic resources to the user account based on the preference parameters corresponding to each of the sets.

11. A computer-readable medium, on which a computer program is stored, wherein, When the computer program is executed by a processor, it implements the method for recommending electronic resources according to any one of claims 1 to 9.

12. An electronic device, wherein, It includes: One or more processors; A storage device, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, enable the one or more processors to implement the method for recommending electronic resources according to any one of claims 1 to 9.

13. A computer program product, wherein, The computer program product includes a computer program which is stored in a computer-readable storage medium. A processor of a computer device reads and executes the computer program, so that the computer device executes the method for recommending electronic resources according to any one of claims 1 to 9.

Images