Information recommendation method, medium, apparatus, and computing device

Abstract

Embodiments of the present disclosure provide an information recommendation method. The information recommendation method comprises: obtaining a user feature; determining a target member benefit to be recommended from a plurality of member benefits according to the user feature; and sending a recommendation information to a terminal, the recommendation information comprising the target member benefit. By recommending the member benefit to the user in a targeted manner based on the user feature, the method of the present disclosure improves the exposure probability of the part of the member benefits in front of the user based on the user feature, improves the accuracy of displaying the member benefits to the user, and thus is conducive to improving the conversion rate and usage rate of the member benefits. Furthermore, embodiments of the present disclosure provide a medium, a device and a computing device.

Description

Technical Field

[0001] The embodiments of this disclosure relate to the field of computer technology, and more specifically, to information recommendation methods, media, apparatus, and computing devices. Background Technology

[0002] This section is intended to provide background or context for the embodiments of this disclosure as set forth in the claims. The description herein is not intended to be a prior art simply because it is included in this section.

[0003] In internet service applications (such as applications that provide multimedia playback services like songs and videos), membership services are offered to users by selling membership privileges.

[0004] Currently, the application's member center page displays all the member services offered by the application. After entering the member center page, users can find the member benefits they need by swiping the page and clicking on member benefits.

[0005] However, under the above approach, as the types of membership benefits increase, many benefits are difficult for users to discover and perceive, resulting in low conversion and usage rates. For example, apart from popular membership benefits such as "ad-free membership," "free downloads," and "high-quality audio" that are well-known to users, other membership benefits are almost transparent to them, and users do not understand what services these membership benefits provide. Summary of the Invention

[0006] This disclosure provides an information recommendation method, medium, apparatus, and computing device to address the problem that membership benefits are difficult for users to discover and perceive, resulting in low conversion and usage rates of membership benefits.

[0007] In a first aspect of the present disclosure, an information recommendation method is provided, applied to a server. The information recommendation method includes: acquiring user characteristics; determining a target membership benefit to be recommended from multiple membership benefits based on the user characteristics; and sending recommendation information to a terminal, the recommendation information including the target membership benefit.

[0008] In one embodiment of this disclosure, determining a target membership benefit to be recommended from multiple membership benefits based on user characteristics includes: determining recommended resources in a digital resource library based on user characteristics; and determining a target membership benefit in a membership benefits library containing multiple membership benefits based on the recommended resources.

[0009] In another embodiment of this disclosure, the user characteristics include user behavior tags. Determining recommended resources in the digital resource library based on user characteristics includes: determining digital resources in the digital resource library that match the user behavior tags; and determining the recommended resources as digital resources that match the user behavior tags.

[0010] In another embodiment of this disclosure, the user behavior tag includes at least one of the following: playback behavior tag, purchase behavior tag, and rights usage behavior tag. In the digital resource library, determining the digital resource that matches the user behavior tag includes: determining the digital resource that matches the playback behavior tag in the digital resource library; and / or, determining the digital resource that matches the purchase behavior tag in the digital resource library; and / or, determining the digital resource that matches the rights usage behavior tag in the digital resource library.

[0011] In another embodiment of this disclosure, the digital resource library further includes content tags for recommended resources, and the membership rights library further includes rights tags for multiple membership rights. Based on the recommended resources, the target membership rights are determined in the membership rights library, including: in the membership rights library, performing similarity matching between the content tags of the recommended resources and the rights tags of the multiple membership rights; determining the target membership rights as membership rights whose rights tags have a similarity greater than a similarity threshold with the content tags of the recommended resources.

[0012] In another embodiment of this disclosure, the recommendation information includes a membership benefits package. Before sending the recommendation information to the terminal, the method further includes: combining the target membership benefits to obtain a membership benefits package.

[0013] In another embodiment of this disclosure, the target member benefits are combined to obtain a member benefits package, including: screening and / or sorting the target member benefits; and combining the processed target member benefits to obtain a member benefits package.

[0014] In another embodiment of this disclosure, the screening and / or sorting of target member benefits includes: determining the weight corresponding to the target member benefits; and screening and / or sorting the target member benefits according to the weight corresponding to the target member benefits.

[0015] In another embodiment of this disclosure, the recommendation information further includes a recommendation text for the target member's benefits. Before sending the recommendation information to the terminal, the method further includes: generating a recommendation text for the target member's benefits based on the recommended resources and the preset text template corresponding to the target member's benefits.

[0016] In a second aspect of the present disclosure, an information recommendation method is provided, applied to a terminal. The information recommendation method includes: receiving recommendation information from a server, the recommendation information including target membership benefits, the target membership benefits being related to user characteristics; and displaying the target membership benefits in response to the recommendation information.

[0017] In one embodiment of this disclosure, the recommendation information includes a membership benefits package, which contains target membership benefits. In response to the recommendation information, displaying the target membership benefits includes: in response to the recommendation information, displaying the membership benefits package.

[0018] In another embodiment of this disclosure, the recommendation information further includes a recommendation text for the target member's benefits. After receiving the recommendation information from the server, the method further includes:

[0019] In response to recommended information, display promotional text highlighting the benefits and privileges of the target member.

[0020] In yet another embodiment of this disclosure, before receiving recommendation information from the server, the method further includes: in response to a request to access a member page, sending a member benefits recommendation request to the server.

[0021] In a third aspect of the present disclosure, a computer-readable storage medium is provided, which stores computer-executable instructions that, when executed by a processor, implement the information recommendation method as described in the first aspect or any embodiment thereof, or implement the information recommendation method as described in the second aspect or any embodiment thereof.

[0022] In a fourth aspect of the present disclosure, an information recommendation device is provided, applied to a server. The information recommendation device includes: an acquisition unit for acquiring user characteristics; a determination unit for determining a target membership benefit to be recommended from multiple membership benefits based on the user characteristics; and a sending unit for sending recommendation information to a terminal, the recommendation information including the target membership benefit.

[0023] In one embodiment of this disclosure, the determining unit is specifically used to: determine recommended resources in a digital resource library based on user characteristics; and determine target membership benefits in a membership benefits library based on the recommended resources, wherein the membership benefits library contains multiple membership benefits.

[0024] In another embodiment of this disclosure, the user features include user behavior tags, and the determining unit is specifically used to: determine digital resources that match the user behavior tags in the digital resource library; and determine the recommended resources as digital resources that match the user behavior tags.

[0025] In another embodiment of this disclosure, the user behavior tag includes at least one of the following: playback behavior tag, purchase behavior tag, and rights usage behavior tag. The determining unit is specifically used to: determine digital resources in the digital resource library that match the playback behavior tag; and / or, determine digital resources in the digital resource library that match the purchase behavior tag; and / or, determine digital resources in the digital resource library that match the rights usage behavior tag.

[0026] In another embodiment of this disclosure, the digital resource library further includes content tags for recommended resources, and the membership rights library further includes rights tags for multiple membership rights. The determining unit is specifically used to: perform similarity matching between the content tags of recommended resources and the rights tags of multiple membership rights in the membership rights library; and determine the target membership rights as membership rights whose rights tags have a similarity greater than a similarity threshold with the content tags of recommended resources.

[0027] In another embodiment of this disclosure, the recommended information includes a membership benefits package, and the information recommendation device further includes a combination unit for combining target membership benefits to obtain a membership benefits package.

[0028] In another embodiment of this disclosure, the combining unit is specifically used for: screening and / or sorting the target member benefits; and combining the processed target member benefits to obtain a member benefit package.

[0029] In another embodiment of this disclosure, the combining unit is specifically used to: determine the weight corresponding to the target member benefits; and perform screening and / or sorting processing on the target member benefits according to the weight corresponding to the target member benefits.

[0030] In another embodiment of this disclosure, the recommendation information further includes a recommendation text for the target member's rights and benefits. The information recommendation device further includes a text generation unit, used to generate a recommendation text for the target member's rights and benefits based on the recommendation resources and the preset text template corresponding to the target member's rights and benefits.

[0031] In a fifth aspect of the present disclosure, an information recommendation device is provided, applied to a terminal. The information recommendation device includes: a receiving unit for receiving recommendation information from a server, the recommendation information including target membership benefits, the target membership benefits being related to user characteristics; and a display unit for displaying the target membership benefits in response to the recommendation information.

[0032] In one embodiment of this disclosure, the recommendation information includes a membership benefits package, which contains target membership benefits. The display unit is specifically used to: display the membership benefits package in response to the recommendation information.

[0033] In another embodiment of this disclosure, the recommendation information further includes a recommendation text for the target member's benefits, and the display unit is further configured to: display the recommendation text for the target member's benefits in response to the recommendation information.

[0034] In another embodiment of this disclosure, the information recommendation device further includes: a sending unit, configured to send a membership benefits recommendation request to the server in response to a request to access a membership page.

[0035] In a sixth aspect of this disclosure, a computing device is provided, comprising: at least one processor and a memory; the memory storing computer execution instructions; the at least one processor executing the computer execution instructions stored in the memory, such that the at least one processor performs an information recommendation method as described in the first aspect or any embodiment thereof, or such that the at least one processor performs an information recommendation method as described in the second aspect or any embodiment thereof.

[0036] In this embodiment, membership benefits to be recommended to users are determined based on user characteristics, and recommendation information is sent to the terminal according to these benefits. Therefore, compared to displaying all membership benefits on the membership page, this embodiment recommends membership benefits to users in a targeted manner based on user characteristics, improving the accuracy of membership benefit display, increasing the likelihood of membership benefits being exposed to users, attracting user attention, and ultimately improving the conversion rate and usage rate of membership benefits. Attached Figure Description

[0037] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which:

[0038] Figure 1 A schematic diagram illustrating an application scenario provided according to an embodiment of this disclosure is shown.

[0039] Figure 2 A schematic flowchart of an information recommendation method provided according to an embodiment of the present disclosure is shown.

[0040] Figure 3 An example diagram illustrating user behavior analysis provided according to an embodiment of this disclosure is shown schematically;

[0041] Figure 4 A schematic flowchart of an information recommendation method provided according to another embodiment of this disclosure is shown;

[0042] Figure 5 A flowchart illustrating an information recommendation method provided according to an embodiment of this disclosure is shown schematically.

[0043] Figure 6 A schematic diagram of the structure of a storage medium provided according to an embodiment of the present disclosure is shown.

[0044] Figure 7 A schematic diagram of the structure of an information recommendation device according to an embodiment of the present disclosure is shown.

[0045] Figure 8 A schematic diagram of the structure of an information recommendation device according to yet another embodiment of the present disclosure is shown;

[0046] Figure 9 A schematic diagram of the structure of a computing device provided according to an embodiment of the present disclosure is shown.

[0047] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0048] The principles and spirit of this disclosure will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are given merely to enable those skilled in the art to better understand and implement this disclosure, and are not intended to limit the scope of this disclosure in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0049] Those skilled in the art will recognize that embodiments of this disclosure can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0050] According to embodiments of this disclosure, an information recommendation method, medium, apparatus, and computing device are proposed.

[0051] In this article, it is important to understand the following terms and their meanings:

[0052] (1) Membership benefits: Privileges offered to members, such as coupons, welfare vouchers, sound effects and animations, personalized skins, and avatar decorations. Generally, different membership benefits are available to different membership levels.

[0053] (2) Membership benefits package: Aggregated content that includes at least two membership benefits and is displayed to users.

[0054] (3) Digital resources: Data resources owned by service providers that can be provided to users in the form of electronic data. For example, music applications possess various resources such as audio and video, comments, sound effects, performances, live broadcasts, and karaoke.

[0055] Furthermore, the number of any elements in the accompanying drawings is for illustrative purposes only and not for limitation, and any naming is for distinction only and has no limiting meaning.

[0056] The principles and spirit of this disclosure will be explained in detail below with reference to several representative embodiments. Invention Overview

[0058] Currently, all membership benefits corresponding to different membership levels on the application are mainly displayed on a specific page (such as the member's personal center page, member purchase page, etc.).

[0059] The inventors discovered that as membership levels become more detailed and the types of membership benefits increase, the above display methods fail to attract users' attention to membership benefits. As a result, most membership benefits are transparent to users, meaning that users are unaware of the services (or value) provided by most membership benefits. This leads to low conversion and usage rates of membership benefits, which is detrimental to the operation of membership benefits.

[0060] In considering solutions to the above problems, the inventors discovered that in technologies related to information recommendation, users are usually categorized based on their characteristics, and then digital resources are recommended in a targeted and personalized manner according to the category group to which the user belongs (such as classifying movie theater users and recommending movies in a personalized manner according to the category group to which the user belongs). However, this recommendation method has not been applied to membership benefits.

[0061] Therefore, this disclosure is based on the idea of ​​information recommendation. On the server side, according to user characteristics, the target membership benefits to be recommended are determined from multiple membership benefits. The target membership benefits are sent to the terminal in the form of recommendation information, thereby realizing personalized recommendations of membership benefits, arousing users' attention to membership benefits, and thus improving the conversion rate and usage rate of membership benefits.

[0062] After introducing the basic principles of this disclosure, various non-limiting embodiments of this disclosure will be described in detail below.

[0063] Application Scenarios Overview

[0064] First refer to Figure 1 , Figure 1The illustration shows an application scenario provided according to an embodiment of the present disclosure. The devices involved in the application scenario include a server 101 and a terminal 102, and the server 101 and the terminal 102 can communicate via a network.

[0065] Among them, server 101 is, for example, the product server of the application, and user data, business data, etc. of the application are deployed in server 101, while terminal 102 has the application client installed.

[0066] Among them, server 101 can send to multiple terminals 102 ( Figure 1 Taking a terminal as an example, the system provides services related to an application to the user. For example, the application is a music playback application. The user opens the application on terminal 102 and performs operations. During this process, terminal 102 interacts with server 101 to provide services such as music playback and music recording to the user.

[0067] The terminal can be a personal digital assistant (PDA) device, a handheld device with wireless communication capabilities (such as a smartphone or tablet), a computing device (such as a personal computer or PC), an in-vehicle device, a wearable device (such as a smartwatch or smart bracelet), or a smart home device (such as a smart display device).

[0068] Exemplary methods

[0069] The following is combined Figure 1 Application scenarios, refer to Figure 2-5 This document describes an information recommendation method based on exemplary embodiments of the present disclosure. It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of the present disclosure, and the embodiments of the present disclosure are not limited in any way. Rather, the embodiments of the present disclosure can be applied to any applicable scenario.

[0070] refer to Figure 2 , Figure 2 A schematic flowchart illustrating an information recommendation method according to an embodiment of this disclosure is shown. Figure 2 As shown, information recommendation methods include:

[0071] S201, The server obtains user characteristics.

[0072] User characteristics reflect a user's features and / or preferences (or interests) in one or more aspects.

[0073] In one example, user characteristics include identity features such as age, gender, and occupation.

[0074] In another example, user characteristics include user behavioral characteristics (or user behavioral preferences, or user interests) in one or more aspects. For example, a user's music listening preferences, video viewing preferences, etc.

[0075] In this embodiment, the terminal can collect user information after user authorization. This user information may include user identity information and / or user behavior information. The terminal sends the collected user information to the server. The server extracts user characteristics from the user information from the terminal and can also save and retrieve these user characteristics.

[0076] To ensure the accuracy of user characteristics, the terminal can collect user information in real time or at preset time intervals, and the server updates the user characteristics periodically based on the user information fed back by the terminal. For example, the server updates the user characteristics once a day.

[0077] S202. Based on user characteristics, the server determines the target membership benefits to be recommended from among multiple membership benefits.

[0078] In this embodiment, after obtaining user characteristics, the server can determine the membership benefits that match the user characteristics from multiple membership benefits based on the user characteristics, and determine the membership benefits to be recommended as the membership benefits that match the user characteristics. For the sake of brevity, the membership benefits to be recommended are referred to as target membership benefits.

[0079] In one example, multiple membership benefits (or their value, describing the services or value they provide to users) can be matched with user characteristics using text matching to determine the target membership benefits to be recommended. These benefits are those whose content and user characteristics have a similarity greater than a similarity threshold. The content of a membership benefit includes information describing its value; for example, the benefit of "song downloads" could include "free download of a vast library of songs on the site."

[0080] The target membership benefits can be one or more. For example, when user characteristics indicate that a user enjoys both listening to music and attending concerts, the target membership benefits can include membership benefits related to listening to music, as well as membership benefits related to concert tickets.

[0081] S203. The server sends recommendation information to the terminal, which includes the target membership benefits.

[0082] In this embodiment, after determining the target member benefits, the server sends recommendation information to the terminal. The recommendation information includes the target member benefits. Specifically, the recommendation information includes the name of the target member benefits. In addition, optionally, the recommendation information may also include the content of the target member benefits.

[0083] For example, when the target membership benefits include two membership benefits, "song download" and "exclusive member song benefits", the recommended information may include the benefit names "song download" and "exclusive member songs", and may also include the benefit content of "song download" (e.g., "free download of a large number of songs on the site") and the benefit content of "exclusive member songs" (e.g., "free listening to exclusive member songs").

[0084] Optionally, the recommendation information may also include the target membership level corresponding to the target membership benefits, so that users can understand what they can enjoy after reaching the target membership level.

[0085] S204. The terminal responds to the recommended information and displays the target member benefits.

[0086] In this embodiment, after receiving recommendation information from the server, the terminal retrieves the target membership benefits to be recommended from the recommendation information and displays the target membership benefits on its own display screen, making the target membership benefits noticeable to the user at the terminal. Since the target membership benefits are determined based on user characteristics, compared to randomly displaying membership benefits or displaying all membership benefits, recommending target membership benefits to the user can more accurately target user preferences and needs, and can more accurately stimulate the user's interest in the target membership benefits, enabling the user to further understand the target membership benefits.

[0087] In this embodiment, the server determines the target membership benefits to be recommended based on user characteristics and sends these benefits to the terminal as recommendation information. The terminal then displays the received target membership benefits, achieving personalized recommendations and making it easier for users to focus on membership benefits related to their interests and needs. If the target membership benefit is a benefit under a membership level that the user has already purchased, the recommendation scheme can remind the user to pay attention to and use the membership benefits they already enjoy. If the target membership benefit is a benefit under a membership level that the user has not purchased, the recommendation scheme helps the user to more accurately purchase membership levels. Therefore, this embodiment can better guide and enhance users' perception of the value of membership benefits, which is conducive to improving the conversion rate and usage rate of membership benefits, making the most of membership benefits, and allowing users to feel that they are getting "more than their money's worth."

[0088] In some embodiments, where user features include user behavior features, user behavior features may be represented as user behavior labels; in other words, user features include user behavior labels.

[0089] In this embodiment, with the user's authorization, the server can pre-collect user behavior information. For example, user behavior information can be a user's behavior log within the application, including user behavior data related to one or more aspects of the services provided by the application. Taking a music playback application as an example, the behavior log contains behavioral data of multiple user behaviors such as music playback and music purchase. After obtaining the user behavior information, the server performs data analysis processing (e.g., keyword extraction) to obtain user behavior tags.

[0090] When the application is an audio / video playback application, the user behavior tags may optionally include at least one of the following: playback behavior tags, purchase behavior tags, and rights usage behavior tags.

[0091] Among them, the playback behavior tag is obtained by analyzing and processing user playback behavior data, the purchase behavior tag is obtained by analyzing and processing user purchase behavior data, and the rights usage behavior tag is obtained by analyzing and processing user rights usage behavior data.

[0092] Furthermore, playback behavior can include song playback behavior, music video (MV) playback behavior, etc. Data processing and analysis of song playback behavior and MV playback behavior can reveal playback behavior tags such as the user's preferred song style, artist, and language.

[0093] Furthermore, purchasing behavior can include song purchases, performance purchases, membership purchases, etc. During data analysis of purchasing behavior, the number of purchases by users can be recorded for different behaviors. Users can be categorized based on the number of purchases, resulting in user classification tags for each purchasing behavior. These tags could be such as light users, moderate users, and heavy users. For example, a user who purchases songs more than 10 times but less than 30 times is classified as a light user; a user who purchases performances more than 30 times but less than 50 times is classified as a moderate user. In this way, user classification tags are obtained for different purchasing behaviors, and these tags are used as purchasing behavior labels.

[0094] Furthermore, the usage of rights can include various types of rights usage behaviors. For example, taking a music playback application as an example, rights usage behaviors can include the use of sound effects and animations, the use of avatars and widgets, the use of personalized skins, and so on. By analyzing the user's sound effect and animation usage behaviors, the user's previously used sound effects and animations can be identified, and the categories to which these sound effects and animations belong can be determined as rights usage behavior tags. Similarly, by analyzing the use of avatars and widgets and personalized skins, the user's previously used avatars and widgets and personalized skins can be identified, and the categories to which these avatars and widgets belong can be determined as rights usage behavior tags.

[0095] For example, see reference Figure 3 , Figure 3 A user behavior analysis graph provided according to an embodiment of this disclosure is illustrated schematically. For example... Figure 3 As shown: User behavior analysis can include analysis of playback behavior (such as listening to songs, playing music videos), purchase behavior, and rights usage behavior. Playback behavior analysis can identify user preferences for song styles, artists, languages, etc., which can be used as user behavior tags. Purchase behavior analysis can, for example, analyze user preferences for purchasing performances within the application, such as the type of performance purchased and the artist associated with it; these can also be used as user behavior tags. In addition, the user categories obtained by classifying users based on the number of purchases mentioned above can also be used as user behavior tags. Rights usage behavior analysis includes analysis of sound effects and animations usage, avatar and accessory usage, personalized skin usage, and other rights usage behaviors. This analysis can identify categories of sound effects and animations, avatar and accessory, personalized skin, and other rights that users have previously liked; these categories can be used as user behavior tags.

[0096] Optionally, the server can store the analyzed user behavior tags in a user behavior tag library, and can also update the user behavior tag library every preset period based on newly collected user behavior information.

[0097] In some embodiments, the timing of acquiring user characteristics is at least one of the following:

[0098] In one possible implementation, the server obtains the updated user characteristics after updating the user characteristics, so as to update the membership benefits to be recommended in a timely manner based on the updated user characteristics.

[0099] In another possible implementation, the server responds to the terminal's membership benefit recommendation request by obtaining pre-stored user characteristics to determine the membership benefits to be recommended when the terminal requests membership benefit recommendations, thus ensuring the accuracy of the recommended membership benefits.

[0100] In another possible implementation, after detecting that a user has logged into the application, the server obtains the user characteristics of the currently logged-in user, thereby promptly determining the membership benefits to be recommended after the user logs in.

[0101] In some embodiments, the timing of sending the recommendation information is any of the following:

[0102] In one possible implementation, after determining the target member's benefits, the server generates recommendation information and sends the recommendation information to the terminal.

[0103] In another possible implementation, the server pre-determines the target membership benefits (based on pre-stored user characteristics). In response to a terminal's membership benefit recommendation request, the server sends recommendation information to the terminal. This improves the accuracy of the timing of recommendation information delivery by sending the recommendation information only when the terminal requests it.

[0104] In some embodiments, the target membership benefits may be displayed in any of the following ways:

[0105] In one possible implementation, a benefits recommendation page is displayed, showing the benefits for the target member. This benefits recommendation page is used solely to display the benefits for the target member.

[0106] Another possible implementation is to display the referral benefits for target members via a pop-up window.

[0107] In another possible implementation, the target membership benefits are displayed in a preset position on the membership page. This allows users to see the target membership benefits on the membership purchase page when they are interested in learning about or purchasing membership benefits, thus improving the ease with which users can understand the target membership benefits.

[0108] In some embodiments, the terminal may send a membership benefits recommendation request to the server in response to a request to access a membership page. Specifically, the terminal receives the user-triggered request to access the membership page when the user clicks to enter the page. Therefore, recommending membership benefits upon detecting a user accessing the membership page improves the appropriateness and accuracy of the timing of such recommendations, making it easier for the user to view the recommended membership benefits on the page. The membership page, for example, is a membership purchase page.

[0109] refer to Figure 4 , Figure 4 A schematic flowchart illustrating an information recommendation method according to another embodiment of this disclosure is shown. Figure 4 As shown, information recommendation methods may include:

[0110] S401, The server obtains user characteristics.

[0111] The implementation principle and technical effects of S401 can be referred to in the aforementioned embodiments, and will not be repeated here.

[0112] S402. The server determines recommended resources from the digital resource database based on user characteristics.

[0113] The digital resource library contains multiple digital resources.

[0114] The data resource library can be dynamically updated. As business changes and network information updates, the digital resource library needs to be updated (including adding, deleting, and / or modifying digital resources). Therefore, manual intervention can be implemented in the digital resource library based on factors such as business changes and network information updates. For example, in the digital resource library associated with a song playback application, developers can add digital resources when new songs or performances are released.

[0115] In this embodiment, since user characteristics reflect user traits and / or preferences, different users have different user characteristics and therefore different preferred digital resources. For example, different users may like different songs. Therefore, after obtaining user characteristics, the server can identify digital resources in the digital resource library that match the user characteristics (i.e., those matching the user characteristics) and determine the recommended resources as those that match the user characteristics. This allows for the targeted determination of digital resources suitable for the user.

[0116] In one possible implementation, S402 includes: classifying users based on user characteristics to determine user categories, and determining recommended resources in a digital resource library based on user categories. For example, if the user category is teenagers, then recommended resources suitable for teenagers are determined in the digital resource library.

[0117] In another possible implementation, where user characteristics include user behavior tags, S402 includes: identifying digital resources in the digital resource library that match the user behavior tags; and determining the recommended resource as a digital resource that matches the user behavior tags. This improves the accuracy of recommended resources based on user behavior preferences. The user behavior tags can be referred to in the foregoing embodiments and will not be repeated here.

[0118] In this implementation, user behavior tags can be matched with digital resources in the digital resource library to obtain digital resources that match the user behavior tags, and these digital resources can be identified as recommended resources.

[0119] Optionally, the digital resource library also includes content tags for the digital resources. These content tags reflect the data characteristics of the digital resources. For example, song content tags may include the song title, composer, lyricist, singer, album, genre, and language. In this case, the content tags of the digital resources can be matched with user behavior tags in the digital resource library to determine recommended resources that have a similarity score greater than a similarity threshold between their content tags and user behavior tags. Alternatively, the digital resources can be sorted from highest to lowest similarity between their content tags and user behavior tags to determine the top preset number of recommended resources.

[0120] In another possible implementation, where user behavior tags include at least one of playback behavior tags, purchase behavior tags, and rights usage behavior tags, the process of identifying digital resources matching the user behavior tags in the digital resource library includes: identifying digital resources matching the playback behavior tags in the digital resource library; and / or, identifying digital resources matching the purchase behavior tags in the digital resource library; and / or, identifying digital resources matching the rights usage behavior tags in the digital resource library. This further improves the accuracy of identifying recommended resources in the digital resource library.

[0121] The process of determining digital resources that match playback behavior tags, purchase behavior tags, and rights usage behavior tags is similar to that of determining digital resources that match user behavior tags, and will not be elaborated further.

[0122] S403. The server determines the target member benefits from the member benefits library based on the recommended resources.

[0123] The membership benefits library contains multiple membership benefits. For example, the membership benefits library can store the names and details of multiple membership benefits. Taking a music playback application as an example, the membership benefits library for that application can contain the following content:

[0124]

[0125]

[0126] The membership benefits database can be dynamically updated. As business changes, market shifts, and technological advancements occur, the database needs to be updated (e.g., adding, deleting, and / or modifying benefits) to provide users with richer, more comprehensive, and personalized membership benefits. Therefore, manual intervention in the membership benefits database can be implemented based on business changes, market shifts, and updates to technical and network information. For example, in the digital resource library associated with a music playback application, developers can add, delete, and / or modify membership benefits based on changes requested by operations personnel.

[0127] In this embodiment, after determining the recommended resource, the server can match the recommended resource with multiple membership benefits in the membership benefits library to determine the target membership benefit that matches the recommended content. Since the recommended resource is determined based on user characteristics, it is a prediction to some extent of the user's current and / or future demand for digital resources (e.g., access needs). Simply put, the recommended resource reflects the user's current and / or future digital resource needs. Therefore, determining the target membership benefit based on the recommended resource ensures that the target membership benefit better matches the user's current and / or future digital resource needs, allowing for the recommendation of suitable membership benefits in advance, and enabling the user to understand the membership benefits related to the recommended resource before acquiring it.

[0128] For example, taking a music playback application as an example, the recommended resource determined based on user characteristics is the performance of singer A, and the target membership benefits determined based on the recommended resource are ticket privileges related to the performance.

[0129] Optionally, the membership benefits database also includes multiple membership benefit tags. These tags can be obtained by analyzing the content of membership benefits (e.g., extracting keywords), and can also be determined as the benefit names of the membership benefits.

[0130] In one possible implementation, where the digital resource library also includes content tags for recommended resources, and the membership benefits library also includes benefit tags for multiple membership benefits, step S403 includes: performing similarity matching between the content tags of recommended resources and the benefit tags of multiple membership benefits in the membership benefits library; and determining the target membership benefit as the membership benefit whose benefit tag has a similarity greater than a similarity threshold with the content tag of the recommended resource. Thus, by tagging recommended resources and membership benefits and combining this with similarity matching, the efficiency and accuracy of determining the target membership benefit are improved.

[0131] S404. The server sends recommendation information to the terminal, which includes the target membership benefits.

[0132] The implementation principle and technical effects of S404 can be referred to in the aforementioned embodiments, and will not be repeated here.

[0133] S405: The terminal responds to the recommended information and displays the target member benefits.

[0134] The implementation principle and technical effects of S405 can be referred to in the aforementioned embodiments, and will not be repeated here.

[0135] In this embodiment, the server determines recommended resources from the digital resource library based on user characteristics, and then determines target membership benefits from the membership benefits library based on the recommended resources. The target membership benefits are then sent to the terminal as recommendation information, and the terminal displays the received target membership benefits. This not only achieves personalized recommendations of membership benefits but also establishes the association between recommended benefits and recommended resources, recommending membership benefits related to the user's interests and digital resource needs, thereby improving the conversion rate and usage rate of membership benefits.

[0136] Based on any of the foregoing embodiments, some extended embodiments of the information recommendation method are provided below.

[0137] In some embodiments, the server combines target membership benefits into a membership benefit package, and then sends recommendation information containing the membership benefit package to the terminal. The terminal responds to the recommendation information and displays the membership benefit package. Therefore, when there are multiple target membership benefits, compared to recommending multiple target membership benefits to the user piece by piece, aggregating multiple target membership benefits into a membership benefit package and then recommending it to the user improves the relevance of multiple target membership benefits in the display, enhances the convenience for users to browse target membership benefits, and saves user time. For example, a membership benefit recommendation area can be reserved on the membership purchase page, displaying the membership benefit package and listing multiple target membership benefits within the package. When users interact with this area, they can view multiple target membership benefits.

[0138] In one possible implementation, the server combines target membership benefits to obtain a membership benefit package. This includes: the server filtering and / or sorting the target membership benefits; and combining the processed target membership benefits to obtain the membership benefit package. Therefore, considering that different target membership benefits have varying degrees of appeal to users, filtering and / or sorting the target membership benefits allows for the highlighting (or prioritization) of those benefits, ensuring that users primarily see those specific benefits first, ultimately achieving a balanced presentation of membership benefits to users.

[0139] In this implementation, the server filters the target membership benefits, selecting those that are most attractive to users. For example, benefits that the user has previously purchased or used, or benefits with significant discounts or special offers. And / or, the server sorts the target membership benefits, placing more attractive benefits first and less attractive benefits last.

[0140] The server then combines the processed target membership benefits into a membership benefit package and sends a recommendation message containing the membership benefit package to the terminal. The terminal displays the target membership benefits in the membership benefit package according to their order.

[0141] Optionally, the server performs filtering and / or sorting processing on the target member benefits, including: the server determining the weight corresponding to the target member benefits; and filtering and / or sorting the target member benefits according to the weight corresponding to the target member benefits.

[0142] This system pre-assigns weights to multiple membership benefits, and these weights are adjustable. For example, operations staff can assign weights to different membership benefits based on business needs, prioritizing the promotion of certain benefits. When business needs change, the weights can be adjusted accordingly. Therefore, this flexible and adjustable weighting effectively improves the flexibility and accuracy of filtering and / or sorting target membership benefits.

[0143] In this optional method, the server retains member benefits with weights greater than or equal to a weight threshold according to their corresponding weights, thus filtering the member benefits. Alternatively, the server sorts the member benefits in descending order of their corresponding weights, achieving a sorting process. A higher weight indicates a more important member benefit. After sorting, all member benefits can be retained, or only those with weights greater than or equal to the weight threshold can be retained, or only the first preset number of sorted member benefits can be retained. For example, if the number of member benefits is less than or equal to the preset number, all member benefits are retained; otherwise, the first preset number of sorted member benefits are retained.

[0144] In some embodiments, when recommended resources are determined in the digital resource library based on user characteristics, and target membership benefits are determined in the membership benefits library based on the recommended resources, the recommendation information sent by the server to the terminal also includes the recommended resources. This enables personalized recommendations of membership benefits and digital resources to users. The recommended membership benefits are also associated with the recommended digital resources, and the recommended digital resources help users understand the value of the recommended membership benefits, thus improving the recommendation effect of membership benefits.

[0145] Optionally, if the recommendation information sent by the server to the terminal also includes recommended resources, the recommendation information may also include a benefit recommendation message for the target membership benefits, with the recommended resources contained within the benefit recommendation message. In this case, before sending the recommendation information to the terminal, the server can generate the benefit recommendation message for the target membership benefits based on a preset message template corresponding to the recommended resources and the target membership benefits. After receiving the recommendation information, the terminal responds by displaying the target membership benefits and their recommendation message. The benefit recommendation message describes the relationship between the target membership benefits and the recommended resources; furthermore, it describes the value that the target membership benefits can bring to the recommended resources (e.g., discounts). Thus, by using the benefit recommendation message, the effectiveness of combining membership benefits with digital resources is improved, making membership benefits easier for users to understand.

[0146] In this embodiment, corresponding text templates can be pre-set for multiple membership benefits, i.e., corresponding preset text templates. The preset text module reserves a first text content that can be flexibly edited (or flexibly replaced) and a fixed second text content. The first text content describes the digital resources and the value generated by using membership benefits on those resources, while the second text content describes the membership benefits themselves. After determining recommended resources and identifying target membership benefits based on those resources, the server can adjust the preset text template corresponding to the target membership benefit based on the content tags of the recommended resources and the benefit tags of the target membership benefit, thus obtaining the recommended text for the target membership benefit.

[0147] For example, if the recommended resource is a performance by singer A, and the ticket price is Y yuan, and the target membership benefit determined based on this recommended resource is "ticketing privileges", and the preset copy template for "ticketing privileges" is "Singer A has a performance, members can purchase tickets at a 10% discount, saving X yuan per year", then the generated recommendation copy for the "ticketing privileges" can be "Singer A has a performance, members can purchase tickets at a 10% discount, saving Y*0.1 yuan per year".

[0148] For example, if the recommended resource is sound effect B, and the target membership benefit determined based on this recommended resource is "sound effects and animations", and the preset copy template for "sound effects and animations" is "sound effect B matches your listening style. Members can try it for free and add some rhythm to their listening experience", then the recommended copy for the "sound effects and animations" benefit can be generated as "sound effect B matches your listening style. Members can try it for free and add some rhythm to their listening experience".

[0149] For example, if the recommended resource is the song "C", and the target membership benefit determined based on this recommended resource is "exclusive member song", and the preset copy template corresponding to "exclusive member song" is "the recently popular song 'XX' can be listened to by members", then the benefit recommendation copy for "exclusive member song" can be generated as "the recently popular song 'C' can be listened to by members".

[0150] For example, if the recommended resources are products from some external partners, and the target member benefits determined based on these recommended resources are "welfare privileges", and the preset copy template for "welfare privileges" is "Exclusive cooperation benefits, take them all", then the recommended copy for "welfare privileges" can be generated as "Exclusive cooperation benefits from XX partner, take them all".

[0151] For example, see reference Figure 5 , Figure 5 A flowchart illustrating an information recommendation method according to an embodiment of this disclosure is shown schematically. Figure 5 Take, for example, the information recommendation system for users of music apps.

[0152] like Figure 5As shown: First, user listening behavior can be analyzed to obtain user behavior tags. These tags include multiple labels related to user preferences such as music genre, language, artist, and region (the user's location or preferred regions for performances). Second, after obtaining the user behavior tags, digital resources for recommendation can be determined based on these tags. For example, based on music genre preference, digital resources related to performances, sound effects, and exclusive member songs can be recommended; based on language preference, similar digital resources can be recommended; based on artist preference, similar digital resources can be recommended; and based on region, similar digital resources can be recommended. Next, based on the digital resources... The process involves determining the membership benefits associated with digital resources, i.e., determining the membership benefits used for recommendations. For example, based on a performance, the membership benefit of "ticketing privileges" can be recommended; based on sound effects and animations, the membership benefit of "sound effects and animations" can be recommended; and based on exclusive member songs, the membership benefits of "membership level privileges" and "personalized ringtones" can be recommended. Manual intervention items can also be added, such as manually updating digital resources, manually updating membership benefits, and manually configuring weights for membership benefits. Finally, the terminal aggregates the digital resources and membership benefits used for recommendations (e.g., the text generation process in the above embodiment), and displays the aggregated digital resources and membership benefits to recommend digital resources and membership benefits to users.

[0153] Exemplary media

[0154] After introducing the methods of exemplary embodiments of this disclosure, the following references are made. Figure 6 The storage medium of the exemplary embodiments of this disclosure will be described.

[0155] refer to Figure 6 As shown, the storage medium 60 stores a program product for implementing the above-described method according to embodiments of the present disclosure. This program product may be a portable compact disc read-only memory (CD-ROM) and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto.

[0156] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, 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 thereof.

[0157] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium.

[0158] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN).

[0159] Exemplary device

[0160] Having introduced the medium of exemplary embodiments of this disclosure, the following references are made to... Figure 7-8 The information recommendation apparatus of the exemplary embodiments of this disclosure is described to implement the information recommendation method in any of the above method embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0161] refer to Figure 7 , Figure 7 A schematic diagram of an information recommendation device according to an embodiment of the present disclosure is shown, which is applied to a server. Figure 7 As shown, the information recommendation device includes:

[0162] The acquisition unit 701 is used to acquire user characteristics.

[0163] The determining unit 702 is used to determine the target membership benefit to be recommended from multiple membership benefits based on user characteristics.

[0164] The sending unit 703 is used to send recommendation information to the terminal, which includes the target membership benefits.

[0165] In some embodiments, the determining unit 702 is specifically used to: determine recommended resources in a digital resource library based on user characteristics; and determine target membership benefits in a membership benefits library based on the recommended resources, wherein the membership benefits library contains multiple membership benefits.

[0166] In some embodiments, user characteristics include user behavior tags, and the determining unit 702 is specifically used to: determine digital resources that match the user behavior tags in the digital resource library; and determine recommended resources as digital resources that match the user behavior tags.

[0167] In some embodiments, the user behavior tag includes at least one of the following: playback behavior tag, purchase behavior tag, and rights usage behavior tag. The determining unit 702 is specifically configured to: determine, in the digital resource library, a digital resource that matches the playback behavior tag; and / or, determine, in the digital resource library, a digital resource that matches the purchase behavior tag; and / or, determine, in the digital resource library, a digital resource that matches the rights usage behavior tag.

[0168] In some embodiments, the digital resource library further includes content tags for recommended resources, and the membership rights library further includes rights tags for multiple membership rights. The determining unit 702 is specifically used to: perform similarity matching between the content tags of recommended resources and the rights tags of multiple membership rights in the membership rights library; and determine the target membership rights as membership rights whose rights tags have a similarity greater than a similarity threshold with the content tags of recommended resources.

[0169] In some embodiments, the recommended information includes a membership benefits package, and the information recommendation device further includes a combination unit 704, used to combine the target membership benefits to obtain a membership benefits package.

[0170] In some embodiments, the combination unit 704 is specifically used for: screening and / or sorting the target member benefits; and combining the processed target member benefits to obtain a member benefit package.

[0171] In some embodiments, the combination unit 704 is specifically used to: determine the weight corresponding to the target member benefits; and perform screening and / or sorting processing on the target member benefits according to the weight corresponding to the target member benefits.

[0172] In some embodiments, the recommendation information may also include a benefit recommendation text for the target member benefits, and the information recommendation device may further include: a text generation unit 705, used to generate a benefit recommendation text for the target member benefits based on the recommendation resources and the preset text template corresponding to the target member benefits.

[0173] refer to Figure 8 , Figure 8 A schematic diagram of an information recommendation device according to another embodiment of the present disclosure is shown, which is applied to a terminal. For example... Figure 8 As shown, the information recommendation device includes:

[0174] The receiving unit 801 is used to receive recommendation information from the server. The recommendation information includes target member benefits, which are related to user characteristics.

[0175] Display unit 802 is used to display target member benefits in response to recommendation information.

[0176] In some embodiments, the recommendation information includes a membership benefits package, which contains target membership benefits. The display unit 802 is specifically used to: display the membership benefits package in response to the recommendation information.

[0177] In some embodiments, the recommendation information also includes a benefit recommendation text for the target member's rights, and the display unit 802 is further configured to: display the benefit recommendation text for the target member's rights in response to the recommendation information.

[0178] In some embodiments, the information recommendation device further includes a sending unit 803, configured to send a membership benefits recommendation request to the server in response to a request to access a membership page.

[0179] Exemplary computing device

[0180] Having described the methods, media, and apparatus of exemplary embodiments of this disclosure, the following references... Figure 9 A computing device according to an exemplary embodiment of the present disclosure will be described.

[0181] Figure 9 The computing device 90 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0182] like Figure 9 As shown, the computing device 90 is presented in the form of a general-purpose computing device. The components of the computing device 90 may include, but are not limited to: at least one processing unit 901, at least one storage unit 902, and a bus 903 connecting different system components (including the processing unit 901 and the storage unit 902).

[0183] The 903 bus includes a data bus, a control bus, and an address bus.

[0184] Storage unit 902 may include readable media in the form of volatile memory, such as random access memory (RAM) 9021 and / or cache memory 9022, and may further include readable media in the form of non-volatile memory, such as read-only memory (ROM) 9023.

[0185] Storage unit 902 may also include a program / utility 9025 having a set (at least one) of program modules 9024, such program modules 9024 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0186] The computing device 90 can also communicate with one or more external devices 904 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 905. Furthermore, the computing device 90 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 906. Figure 9 As shown, network adapter 906 communicates with other modules of computing device 90 via bus 903. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with computing device 90, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0187] It should be noted that although several units / modules or sub-units / modules of the information recommendation device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0188] Furthermore, although the operations of the methods disclosed herein are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0189] While the spirit and principles of this disclosure have been described with reference to several specific embodiments, it should be understood that this disclosure is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined for benefit; such division is merely for convenience of expression. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. An information recommendation method, applied to a server, the information recommendation method comprising: Obtain user characteristics; Based on the user characteristics, recommended resources are determined in the digital resource library; In the membership benefits library, the content tags of the recommended resources are matched with the benefit tags of the multiple membership benefits based on similarity. The target membership benefits are defined as membership benefits whose similarity between the benefit tag and the content tag of the recommended resource is greater than a similarity threshold; the membership benefit library contains the multiple membership benefits. Recommendation information is sent to the terminal. The recommendation information includes a membership benefits package, which is obtained by filtering and / or sorting the target membership benefits according to the weights corresponding to the target membership benefits.

2. The information recommendation method according to claim 1, wherein the user features include user behavior tags, and the step of determining recommended resources in the digital resource database based on the user features includes: In the digital resource library, identify digital resources that match the user behavior tags; The recommended resource is determined to be a digital resource that matches the user behavior tag.

3. The information recommendation method according to claim 2, wherein the user behavior tags include at least one of the following: playback behavior tags, purchase behavior tags, and rights usage behavior tags, and the step of determining the digital resources matching the user behavior tags in the digital resource library includes: In the digital resource library, identify digital resources that match the playback behavior tag; And / or, in the digital resource library, identify digital resources that match the purchase behavior tag; And / or, in the digital resource library, identify digital resources that match the rights usage behavior tag.

4. An information recommendation method applied to a terminal, the information recommendation method comprising: Receive recommendation information from the server, the recommendation information including a membership benefits package, the membership benefits package being obtained by filtering and / or sorting the target membership benefits according to the weight corresponding to the target membership benefits; The target membership benefits are those membership benefits in the membership benefits library whose content tags have a similarity greater than a similarity threshold with the recommended resources. The recommended resources are determined in the digital resource library based on user characteristics. The membership benefits library contains the multiple membership benefits. In response to the recommended information, the membership benefits package is displayed.

5. The information recommendation method according to claim 4, further comprising, before receiving recommendation information from the server: In response to a request to access the member page, a member benefits recommendation request is sent to the server.

6. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the information recommendation method as described in any one of claims 1 to 5.

7. An information recommendation device applied to a server, the information recommendation device comprising: The acquisition unit is used to acquire user characteristics; A determining unit is configured to determine recommended resources in a digital resource library based on the user characteristics. In the membership benefits library, the content tags of the recommended resources are matched with the benefit tags of the multiple membership benefits based on similarity; the target membership benefit is determined to be a membership benefit whose benefit tag has a similarity greater than a similarity threshold with the content tag of the recommended resource; the membership benefits library contains the multiple membership benefits. A sending unit is used to send recommendation information to a terminal, the recommendation information including the target membership benefits; the recommendation information includes a membership benefit package, the membership benefit package being obtained by filtering and / or sorting the target membership benefits according to the weights corresponding to the target membership benefits.

8. The information recommendation device according to claim 7, wherein the user features include user behavior tags, and the determining unit is specifically used for: In the digital resource library, identify digital resources that match the user behavior tags; The recommended resource is determined to be a digital resource that matches the user behavior tag.

9. The information recommendation device according to claim 8, wherein the user behavior tags include at least one of the following: playback behavior tags, purchase behavior tags, and rights usage behavior tags, and the determining unit is specifically used for: In the digital resource library, identify digital resources that match the playback behavior tag; And / or, in the digital resource library, identify digital resources that match the purchase behavior tag; And / or, in the digital resource library, identify digital resources that match the rights usage behavior tag.

10. An information recommendation device, applied to a terminal, the information recommendation device comprising: A receiving unit is configured to receive recommendation information from a server, the recommendation information including a membership benefits package, the membership benefits package being obtained by filtering and / or sorting the target membership benefits according to the weights corresponding to the target membership benefits; The target membership benefits are those membership benefits in the membership benefits library whose content tags have a similarity greater than a similarity threshold with the recommended resources. The recommended resources are determined in the digital resource library based on user characteristics. The membership benefits library contains the multiple membership benefits. The display unit is used to display the membership benefits package in response to the recommendation information.

11. The information recommendation device according to claim 10, further comprising: The sending unit is used to send a membership benefits recommendation request to the server in response to a request to access the membership page.

12. A computing device, comprising: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the information recommendation method as described in any one of claims 1 to 5.

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