Information recommendation method, device and electronic device

Abstract

An embodiment of the present invention provides an information recommendation method, device and electronic device, wherein the method includes: a server determines a target user; obtains application associated information of the target user, and mines associated users of the target user based on the application associated information; determines application feature terms that match the associated users, and sends the application feature terms to the client of the target user, so that the client of the target user recommends information based on the application feature terms; since the input difference between the target user and the corresponding associated users is small, based on the application feature terms corresponding to the associated users of the target user, information can be better recommended to the target user, thereby improving the input efficiency of the target user.

Description

Technical Field

[0001] The present invention relates to the technical field of input methods, and in particular to an information recommendation method, device and electronic equipment. Background Art

[0002] With the development of computer technology, electronic devices such as mobile phones and tablet computers are becoming more and more popular, bringing great convenience to people's life, study and work. These electronic devices are usually installed with input method applications (input methods for short), so that users can use the input methods to input information.

[0003] When users use input methods to input, the input methods can recommend entries to users based on the system vocabulary to improve the user's input efficiency. Among them, the entries in the system vocabulary are the entries with high frequency of use by users across the entire network. With the development of information diversification, new entries and new expressions continue to emerge, which increases the differences in expression among different users. The entries recommended by the input method based on the system vocabulary may not meet the user's needs; this results in the user having to input multiple times in segments to complete the entry they want to express, which is inefficient. Summary of the invention

[0004] An embodiment of the present invention provides an information recommendation method to improve input efficiency.

[0005] Correspondingly, the embodiment of the present invention further provides an information recommendation device and an electronic device to ensure the implementation and application of the above method.

[0006] In order to solve the above problem, an embodiment of the present invention discloses an information recommendation method, which specifically includes: a server determines a target user; obtains application associated information of the target user, and mines associated users of the target user based on the application associated information; determines application feature terms that match the associated users, and sends the application feature terms to the client of the target user, so that the client of the target user recommends information based on the application feature terms.

[0007] Optionally, the application association information includes application category information; mining associated users of the target user based on the application association information includes: obtaining application association information of other users, the other users including multiple users; converting the category information of the target user and each other user into corresponding category feature information; determining the association degree information between the target user and each other user based on the category feature information; and determining other users whose association degree information with the target user exceeds an association degree threshold as associated users of the target user.

[0008] Optionally, the category characteristic information is an X-dimensional vector, where X is a positive integer, X is equal to the sum of the application categories, and one dimension corresponds to one category of application; the application association information also includes application usage information; the method further includes: for each of the target user and other users: determining the application weight information corresponding to the user based on the application usage information corresponding to the user; performing weighted calculation on the category characteristic information corresponding to the user based on the application weight information corresponding to the user, and updating the category characteristic information corresponding to the user.

[0009] Optionally, the determining the application feature terms that match the associated user includes: mining application feature terms of each application; and determining the application feature terms that match each application and the associated user.

[0010] Optionally, the mining of associated terms of each application includes: for an application, collecting user terms entered by users from the entire network in the application, the users from the entire network including the target user and other users; for a user term, counting the number of users using the user term, and determining the top M user terms with the highest number of users using the user term as application feature terms of the application, where M is a positive integer.

[0011] Optionally, determining the application feature entries that match each application and the associated user includes: determining, for each application feature entry of each application, the number of associated users using the application feature entry; and determining top N application feature entries with the highest number of associated users using them as the application feature entries that match the application and the associated user, where N is a positive integer.

[0012] Optionally, sending the application feature entry to the client of the target user includes: configuring an application identifier of a corresponding application for the application feature entry; and sending the application feature entry with the application identifier and a corresponding number of associated user usages to the client of the target user.

[0013] An embodiment of the present invention also discloses an information recommendation device, which specifically includes: a user determination module, used to determine a target user; a user mining module, used to obtain application-related information of the target user, and mine associated users of the target user based on the application-related information; an entry delivery module, used to determine an application feature entry that matches the associated user, and send the application feature entry to the client of the target user, so that the client of the target user can recommend information based on the application feature entry.

[0014] Optionally, the application association information includes application category information; the user mining module is used to obtain application association information of other users, and the other users include multiple users; respectively convert the category information of the target user and each other user into corresponding category feature information; based on the category feature information, respectively determine the association information between the target user and each other user; and determine other users whose association information with the target user exceeds an association threshold as associated users of the target user.

[0015] Optionally, the category characteristic information is an X-dimensional vector, where X is a positive integer, X is equal to the sum of the application categories, and one dimension corresponds to one category of application; the application association information also includes application usage information; the device also includes: an information updating module, which is used to: for each of the target user and other users: determine the application weight information corresponding to the user based on the application usage information corresponding to the user; perform weighted calculation on the category characteristic information corresponding to the user based on the application weight information corresponding to the user, and update the category characteristic information corresponding to the user.

[0016] Optionally, the entry delivery module includes: an entry mining submodule for mining application feature entries of each application; and an entry matching submodule for determining application feature entries that match each application with the associated user.

[0017] Optionally, the term mining submodule is used to collect user terms entered by users across the entire network in an application, wherein the users across the entire network include the target user and other users; for a user term, count the number of users using the user term, and determine the top M user terms with the highest number of users using them as application feature terms of the application, where M is a positive integer.

[0018] Optionally, the term matching submodule is used to determine the number of associated users using each application feature term of each application; and determine the top N application feature terms with the highest number of associated users as the application feature terms that match the application and the associated users, where N is a positive integer.

[0019] Optionally, the entry sending module includes: a feature entry sending submodule, which is used to configure an application identifier of a corresponding application for the application feature entry; and send the application feature entry with the application identifier and the corresponding associated user usage number to the client of the target user.

[0020] An embodiment of the present invention also discloses a server, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, wherein the one or more programs include instructions for performing the following operations: determining a target user; obtaining application associated information of the target user, and mining associated users of the target user based on the application associated information; determining application feature terms that match the associated users, and sending the application feature terms to the client of the target user, so that the client of the target user recommends information based on the application feature terms.

[0021] Optionally, the application association information includes application category information; mining associated users of the target user based on the application association information includes: obtaining application association information of other users, the other users including multiple users; converting the category information of the target user and each other user into corresponding category feature information; determining the association degree information between the target user and each other user based on the category feature information; and determining other users whose association degree information with the target user exceeds an association degree threshold as associated users of the target user.

[0022] Optionally, the category characteristic information is an X-dimensional vector, where X is a positive integer, X is equal to the sum of the application categories, and one dimension corresponds to one category of application; the application association information also includes application usage information; and further includes instructions for performing the following operations: for each of the target user and other users: determining the application weight information corresponding to the user based on the application usage information corresponding to the user; performing weighted calculation on the category characteristic information corresponding to the user based on the application weight information corresponding to the user, and updating the category characteristic information corresponding to the user.

[0023] Optionally, the determining the application feature terms that match the associated user includes: mining application feature terms of each application; and determining the application feature terms that match each application and the associated user.

[0024] Optionally, the mining of associated terms of each application includes: for an application, collecting user terms entered by users from the entire network in the application, the users from the entire network including the target user and other users; for a user term, counting the number of users using the user term, and determining the top M user terms with the highest number of users using the user term as application feature terms of the application, where M is a positive integer.

[0025] Optionally, determining the application feature entries that match each application and the associated user includes: determining, for each application feature entry of each application, the number of associated users using the application feature entry; and determining top N application feature entries with the highest number of associated users using them as the application feature entries that match the application and the associated user, where N is a positive integer.

[0026] Optionally, sending the application feature entry to the client of the target user includes: configuring an application identifier of a corresponding application for the application feature entry; and sending the application feature entry with the application identifier and a corresponding number of associated user usages to the client of the target user.

[0027] An embodiment of the present invention discloses an information recommendation method, which specifically includes: a target user's client receives an application feature entry sent by a server, wherein the application feature entry corresponds to an associated user of the target user; obtaining input information of the target user; searching for a target application feature entry that matches the input information, and displaying the target application feature entry.

[0028] Optionally, the application feature entry has an application identifier, and the input information includes input environment information; the displaying of the target application feature entry includes: matching the application identifier of each target application feature entry with the input environment information respectively, and determining the target application feature entry that matches the input environment; and displaying the target application feature entry that matches the input environment information before other target application feature entries.

[0029] Optionally, the receiving of application feature terms sent by the server includes: receiving the application feature terms sent by the server and the number of associated users using the application feature terms; the display of the target application feature terms includes: sorting each target application feature term according to the number of associated users using the term; and displaying each target application feature term according to the sorting result.

[0030] An embodiment of the present invention also discloses an information recommendation device, which specifically includes: an entry receiving module, used to receive application feature entries sent by a server, wherein the application feature entries correspond to associated users of a target user; an information acquisition module, used to obtain input information of the target user; and a recommendation module, used to search for target application feature entries that match the input information and display the target application feature entries.

[0031] Optionally, the application feature entry has an application identifier, and the input information includes input environment information; the recommendation module includes: a first display submodule, used to match the application identifier of each target application feature entry with the input environment information respectively, and determine the target application feature entry that matches the input environment; and display the target application feature entry that matches the input environment information before other target application feature entries.

[0032] Optionally, the entry receiving module is used to receive application feature entries and the number of associated users corresponding to the application feature entries sent by the server; the recommendation module includes: a second display submodule, used to sort each target application feature entry according to the number of associated users; and display each target application feature entry according to the sorting result.

[0033] An embodiment of the present invention also discloses a terminal device, including a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, wherein the one or more programs include instructions for performing the following operations: receiving application feature entries sent by a server, wherein the application feature entries correspond to associated users of a target user; obtaining input information of the target user; searching for a target application feature entry that matches the input information, and displaying the target application feature entry.

[0034] Optionally, the application feature entry has an application identifier, and the input information includes input environment information; the displaying of the target application feature entry includes: matching the application identifier of each target application feature entry with the input environment information respectively, and determining the target application feature entry that matches the input environment; and displaying the target application feature entry that matches the input environment information before other target application feature entries.

[0035] Optionally, the receiving of application feature terms sent by the server includes: receiving the application feature terms sent by the server and the number of associated users using the application feature terms; the display of the target application feature terms includes: sorting each target application feature term according to the number of associated users using the term; and displaying each target application feature term according to the sorting result.

[0036] An embodiment of the present invention further discloses a readable storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the information recommendation method as described in any one of the embodiments of the present invention.

[0037] The embodiments of the present invention include the following advantages:

[0038] In an embodiment of the present invention, the server can determine the target user, then obtain the application associated information of the target user, and mine the associated users of the target user based on the application associated information; then determine the application feature terms that match the associated users, and send the application feature terms to the client of the target user, so that the client of the target user can recommend information based on the application feature terms; since the input difference between the target user and the corresponding associated users is small, based on the application feature terms corresponding to the associated users of the target user, better information recommendations can be made for the target user, thereby improving the input efficiency of the target user. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of steps of an embodiment of a server-side information recommendation method of the present invention;

[0040] Figure 2 It is a flowchart of the steps of an embodiment of a method for recommending information on a client side of the present invention;

[0041] Figure 3 is a flowchart of steps of an information recommendation method embodiment of the present invention;

[0042] Figure 4 It is a structural block diagram of an embodiment of a server-side information recommendation device of the present invention;

[0043] Figure 5 It is a structural block diagram of an optional embodiment of a server-side information recommendation device of the present invention;

[0044] Figure 6 It is a structural block diagram of an embodiment of a client-side information recommendation device of the present invention;

[0045] Figure 7 It is a structural block diagram of an optional embodiment of a client-side information recommendation device of the present invention;

[0046] Figure 8 A structural block diagram of a terminal device for information recommendation according to an exemplary embodiment;

[0047] Fig. 9 It is a structural diagram of a server for information recommendation according to another exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] One of the core concepts of the present invention is that the server sends the application feature entries corresponding to the associated users to the target user; subsequently, the target user's client can make recommendations for the target user based on the application feature entries corresponding to the associated users. Since the input difference between the target user and the associated users is small, the embodiments of the present invention can better recommend information to the target user, thereby improving the input efficiency of the target user.

[0050] The following takes the server side as an example to illustrate the information recommendation method of the embodiment of the present invention.

[0051] Reference Figure 1 , shows a flow chart of a server-side information recommendation method embodiment of the present invention, which may specifically include the following steps:

[0052] Step 102: The server determines the target user.

[0053] In the embodiment of the present invention, when the input efficiency of a certain user needs to be improved, the server can determine the user whose input efficiency needs to be improved as a target user. The target user can be any user in the entire network, and the embodiment of the present invention does not limit this.

[0054] Step 104: Acquire application-related information of the target user, and mine related users of the target user based on the application-related information.

[0055] After determining the target user, the application-related information of the target user can be obtained. The application-related information of the target user can refer to information related to the applications installed on the client of the target user, such as type information of the applications installed on the client of the target user, usage information of the applications installed on the client of the target user, etc., which is not limited in this embodiment of the present invention. Then, based on the application-related information of the target user, the associated users of the target user are mined from other users of the entire network. The associated users can refer to users who are associated with the target user; there can be one or more users, which is not limited in this embodiment of the present invention. The other users can refer to users other than the target user in the entire network.

[0056] Step 106: Determine an application feature entry that matches the associated user, and send the application feature entry to the client of the target user, so that the client of the target user recommends information based on the application feature entry.

[0057] In an embodiment of the present invention, the application feature terms of each application may be determined first, and the application feature terms may refer to terms related to the application; and may be determined based on terms input by users of the entire network in the process of using the application. For example, if the application is a game application, the terms input by users of the entire network, such as game character names, game level names, names of game props, game skill names, etc., may be used as application feature terms for game applications. The terms may include characters and / or words. The various applications may refer to all applications installed on the corresponding clients of users of the entire network.

[0058] Subsequently, the application feature entry that matches the associated user can be determined from the application feature entries of all applications; then the application feature entry that matches the associated user can be sent to the client of the target user. Subsequently, the client of the target user can recommend information based on the application feature entry; since the difference between the target user and the corresponding associated user is small, better recommendations can be made for the target user, thereby improving the input efficiency of the target user.

[0059] In summary, in an embodiment of the present invention, the server can determine the target user, then obtain the application associated information of the target user, and mine the associated users of the target user based on the application associated information; then determine the application feature terms that match the associated users, and send the application feature terms to the client of the target user, so that the client of the target user can recommend information based on the application feature terms; since the input difference between the target user and the corresponding associated users is small, based on the application feature terms corresponding to the associated users of the target user, better information recommendations can be made for the target user, thereby improving the input efficiency of the target user.

[0060] The following takes the client side as an example to illustrate the information recommendation method of the embodiment of the present invention.

[0061] Reference Figure 2 , shows a flowchart of a method for recommending information on a client side according to an embodiment of the present invention, which may specifically include the following steps:

[0062] Step 202: The client of the target user receives the application feature entry sent by the server, where the application feature entry corresponds to an associated user of the target user.

[0063] In the embodiment of the present invention, after the server sends the application feature entry matching the associated user corresponding to the target user to the client corresponding to the target user, the client of the target user can receive the corresponding application entry. Then, after obtaining the input information of the target user, the server can make recommendations based on the application feature entry.

[0064] Step 204: Obtain input information of the target user.

[0065] Step 206: Search for a target application feature entry that matches the input information, and display the target application feature entry.

[0066] After the input method in the target user's client is called up, the input information of the target user can be obtained; the input information can include information related to the input, such as input sequence, candidates on the screen, interaction information, input environment information, etc., which is not limited in this embodiment of the present invention. Then the received application feature terms can be matched with the input information, and the target application feature terms that match the input information can be searched and displayed. Of course, after obtaining the input information, the input information can also be matched with the system terms in the system vocabulary, and the system terms that match the input information can be searched and displayed; this is not limited in this embodiment of the present invention.

[0067] In summary, in the embodiments of the present invention, the client of the target user receives the application feature terms sent by the server, and then after obtaining the input information of the target user, it can search for and display the target application feature terms that match the input information; wherein, the application feature terms correspond to the associated users of the target user. Since the input difference between the target user and the corresponding associated users is small, compared with the prior art that recommends information to users based on the system vocabulary, the embodiments of the present invention can better recommend information to the target user, thereby improving the input efficiency of the target user.

[0068] The information recommendation method of the embodiment of the present invention is described below by taking the server side and the client side as an example.

[0069] Reference Figure 3 , shows a flowchart of an optional embodiment of an information recommendation method of the present invention, which may specifically include the following steps:

[0070] Step 302: The server determines the target user.

[0071] In the above step 104, mining the associated users of the target user based on the application associated information may include the following steps 304 to 310:

[0072] Step 304: The server obtains the application association information of the target user and the application association information of other users; wherein the application association information includes application category information, and the other users may include multiple users.

[0073] In the embodiment of the present invention, the server may obtain the application association information of the target user and the application association information of multiple other users; then, based on the application association information of the target user and the application association information of multiple other users, the server may search for the associated users of the target user from other users of the entire network. The application association information of the other users may refer to information related to the applications installed on the clients of the other users, such as application category information of the applications installed on the clients of the other users, application usage information of the applications installed on the clients of the other users, etc., which is not limited in the embodiment of the present invention.

[0074] In an optional embodiment of the present invention, a user-based collaborative filtering algorithm may be used to determine the associated users of the target user based on the application association information of the target user and the application association information of multiple other users, and reference may be made to steps 306 to 310.

[0075] Step 306: The server converts the category information of the target user and other users into corresponding category feature information.

[0076] In an optional embodiment of the present invention, the application association information may include application category information. In an example of the present invention, the application category information may be obtained by obtaining an application installation list of each user (including the target user and other users), and then obtaining the application category information from the application installation list.

[0077] In an optional embodiment of the present invention, the server may convert the category information corresponding to the target user and each user among multiple other users to obtain category feature information corresponding to each user; the category feature information is a quantitative representation of the category information of the application program, so as to facilitate the determination of the associated users of the target user. Now, taking the target user and one of the multiple other users as an example, a method of converting the category information corresponding to the user into the corresponding category feature information is described. In an embodiment of the present invention, the total number of application categories X can be determined based on the category information of users in the entire network; X is a positive integer. Then, an empty vector with a dimension of X can be created for the user, wherein each dimension of the empty vector corresponds to a category of the application program. Then, based on the category information of the user, the value of the dimension corresponding to the category of the application program of the user in the empty vector is set to a first preset value, and the values ​​of other dimensions in the empty vector are set to a second preset value; and then the category feature information of the user can be obtained, and the category feature information can be a vector of X. Among them, the first preset value and the second preset value can be set as required, for example, the first preset value is 1 and the second preset value is 0.

[0078] Step 308: The server determines the relevance information between the target user and other users respectively according to the category feature information.

[0079] In the embodiment of the present invention, the association information between the target user and each other user can be determined by respectively calculating the similarity information between the category feature information of the target user and the category feature information of each other user. The similarity information between the category feature information of the other user and the category feature information of the target user can be used as the association information between the other user and the target user.

[0080] In an example of the present invention, the similarity information can be calculated by calculating cosine similarity; the expression of calculating the cosine similarity between the category feature information of the target user and the category feature information of another user can be as follows:

[0081]

[0082] Among them, similarity is the cosine similarity, A is the category feature information corresponding to the target user, and B is the category feature information corresponding to other users; the value range of i is 1 to X.

[0083] Of course, the similarity information may also be calculated by calculating Euclidean distance, Jaccard similarity coefficient, cross entropy, and other methods, which are not limited in the embodiment of the present invention.

[0084] In an optional embodiment of the present invention, in order to further improve the accuracy of determining the associated users of the target user, the method may also include:

[0085] Step S2: for the target user and each of the other users: determining the application weight information corresponding to the user according to the usage information of the application corresponding to the user;

[0086] Step S4: weighted calculation is performed on the category characteristic information corresponding to the user according to the application weight information corresponding to the user, and the category characteristic information corresponding to the user is updated.

[0087] In an optional embodiment of the present invention, the application association information may further include application usage information, and the usage information may include multiple information, such as usage frequency, usage duration, and the like.

[0088] Among them, for the target user and each of the multiple other users, the weight value of each application can be determined based on the user's usage information for each application. Among them, the weight value is proportional to the usage information. The greater the usage information, the greater the weight value. Then an empty vector with a dimension of X can be established for the user, and each dimension of the empty vector corresponds to a type of application. Then, the weight values ​​of each application of the user are added to the corresponding dimensions of the empty vector; and the value corresponding to the dimension without adding the weight value in the empty vector is set to a third preset value; and the application weight information corresponding to the user is obtained. Among them, the third preset value can be set to 0 as required, and the embodiment of the present invention is not limited to this.

[0089] In the embodiment of the present invention, the value of each dimension of the category characteristic information of the user may be multiplied by the value of the corresponding dimension of the application weight information to update the category characteristic information. Then, the association information between the target user and other users may be determined respectively based on the updated category characteristic information.

[0090] Step 310: The server determines other users whose association information with the target user exceeds an association threshold as associated users of the target user.

[0091] In the embodiment of the present invention, other users whose association information exceeds the association threshold can be selected from multiple other users as the association information of the target user. The association threshold can be set as required, and the embodiment of the present invention does not limit this.

[0092] Step 312: The server mines application feature terms of each application.

[0093] In the embodiment of the present invention, the server may pre-mine application feature terms of each application, and then directly obtain the predetermined application feature terms of each application after executing step 310. It is also possible to mine application feature terms of each application after executing step 310, which is not limited in the embodiment of the present invention.

[0094] In an embodiment of the present invention, a method for mining application feature terms of each application may be to determine the application feature terms of each application based on user terms of users across the entire network; refer to the following sub-steps:

[0095] Sub-step 22: for an application, collecting user entries input by users of the entire network in the application, wherein users of the entire network include the target user and other users.

[0096] Sub-step 24: for a user term, counting the number of users using the user term, and determining the first M user terms with the highest number of users using them as application feature terms of the application, where M is a positive integer.

[0097] In an embodiment of the present invention, for each program, user terms entered by users of the entire network in the application can be collected; then, for each user's user term, the number of users using the user term is counted. For example, if there are 1 million users in the entire network and the number of users using user term A is 500,000, then the number of users using user term A can be determined to be 500,000. Then, the top M user terms with the highest number of user usages are determined as application feature terms of the application, that is, the user terms used by more users in the application are selected as application feature terms of the application.

[0098] In an embodiment of the present invention, another method of mining application feature terms of each application may be to mine application feature terms of each application through UGC (User Generated Content). For each application, all network users may upload the high-frequency user terms used in the application to the server in advance, and then the server may merge the high-frequency user terms uploaded by all network users to obtain the application feature terms of the application. Of course, the server may also select the application feature terms of the application from the high-frequency user terms uploaded by all network users according to the above sub-steps 22 to 24.

[0099] Step 314: The server determines application feature entries that match each application with the associated user.

[0100] Then, referring to the following sub-steps 42-44, the application feature terms that match each application and the associated user can be found from the application feature terms of each application:

[0101] Sub-step 42: for each application feature entry of each application, determine the number of users associated with the application feature entry.

[0102] Sub-step 44: determine the first N application feature terms with the highest usage count by the associated user as application feature terms that match the application and the associated user, where N is a positive integer.

[0103] In an embodiment of the present invention, the application feature terms used by more associated users in each application can be selected as the application feature terms that match each application with the associated users. For example, the target user has 10 associated users, and application 1 includes 10 application feature terms, namely K1, K2, K3, K4, K5, K6, K7, K8, K9, and K10. The number of associated users using each application feature term is respectively: 1, 3, 3, 6, 4, 2, 5, 7, 8, 7; if N is 5, application feature terms K4, K7, K8, K9, and K10 are used as application feature terms that match application 1 with the associated users.

[0104] Step 316: The server configures the identifier of the corresponding application for the application feature entry.

[0105] Step 318: The server sends the application feature entry with the application identifier and the corresponding associated user usage count to the client of the target user.

[0106] In the embodiment of the present invention, in order for the client of the target user to better recommend to the user, the server can configure the application identifier of the corresponding application for the application feature entry matching the associated user. The application identifier can be used to uniquely identify an application. Then, the application feature entry with the application identifier and the corresponding associated user usage count are sent to the client of the target user.

[0107] Step 320: The client of the target user receives the application feature entry with the application identifier and the corresponding associated user usage number.

[0108] Step 322: The client of the target user obtains input information of the target user.

[0109] In an embodiment of the present invention, the input information obtained by the client of the target user may include input environment information, input sequence (candidate items on the screen) and interaction information; the input environment information may include an application identifier, and of course may also include information of other dimensions such as time information, location information, etc., which is not limited by the embodiment of the present invention.

[0110] Step 324: The target user's client searches for a target application feature entry that matches the input information.

[0111] In an embodiment of the present invention, there may be multiple ways to search for a target application feature entry that matches the input information. In one example, if the input information includes input environment information, input sequence, and interaction information, the acquired input sequence may be matched with the input sequence of each application feature entry to obtain a candidate application feature entry. The candidate application feature entry is then screened based on the input environment information and the interaction information to determine the target application feature entry. The input sequence may include multiple types such as pinyin, strokes, Wubi, etc., which are not limited in the embodiment of the present invention.

[0112] In another example of the present invention, if the input information includes input environment information, candidate items on the upper screen and interaction information, then based on obtaining application feature entries, the candidate items on the upper screen can be associated with the input environment information and the interaction information to determine the corresponding target application feature entries.

[0113] Step 326: The target user's client displays the target application feature entry according to the application identifier of the target application feature entry and / or the number of associated users using the target application feature entry.

[0114] In an example of the present invention, the target application feature entry may be displayed according to the application identifier of the target application feature entry, which may include the following sub-steps 62 to 64:

[0115] Sub-step 62: Match the application identifier of each target application feature entry with the input environment information respectively, and determine the target application feature entry that matches the input environment.

[0116] Sub-step 64: displaying the target application feature entry that matches the input environment information before other target application feature entries.

[0117] In an embodiment of the present invention, the application identifier of each target application feature entry can be matched with the application identifier in the input environment information; the target application feature entry with the same application identifier as the application identifier in the input environment information is determined as the target application feature entry that matches the input environment information. The target application feature entry that matches the input environment information is then displayed before other target application feature entries. Among them, the other target application feature words may refer to entries in the target application feature entries other than the target application feature entry that matches the input environment information. Furthermore, the application feature entries used by the associated user in the application where the target user's client is currently located are displayed before the application feature entries used by the associated user in other applications, so that the target user can quickly select the required information and further improve input efficiency.

[0118] In an example of the present invention, the target application feature entry may also be displayed according to the number of associated users using the target application feature entry, which may include the following sub-steps 82 to 84:

[0119] Sub-step 82: sorting the characteristic terms of each target application program according to the usage number of the associated users.

[0120] Sub-step 84: display the characteristic terms of each target application according to the sorting results.

[0121] In the embodiment of the present invention, each target application feature entry has a corresponding number of associated user usages. Each target application feature entry can be sorted according to the number of associated user usages, for example, sorting each target application feature entry in descending order according to the number of associated user usages. Then, each target application feature entry is displayed according to the sorting result; for example, if the target application feature entry is sorted in descending order, each target application feature entry can be displayed from high to low according to the number of associated user usages. In addition, the application feature entry used by the majority of associated users is displayed before the application feature entry used by the minority of associated users, so that the target user can quickly select the required information, further improving the input efficiency.

[0122] Of course, the above two methods can also be combined to display the target application feature terms. For example, after determining the target application feature terms that match the input environment, the target application feature terms that match the input environment can be sorted according to the number of associated users; and the other application feature terms can be sorted according to the number of associated users. Then, the sorted target application feature terms that match the input environment are displayed before the sorted other application feature terms.

[0123] Of course, in the embodiment of the present invention, the system terms that match the input information can also be found from the system vocabulary, and after finding the system terms that match the input information from the system vocabulary, the target application feature terms can be displayed in combination with the system terms. For example, the target application feature terms can be displayed before the system terms; the target application feature terms and the system terms can also be displayed according to the candidate scores of the target application feature terms (such as those determined based on the number of associated users) and the candidate scores of the system terms, and so on; the embodiment of the present invention does not limit this.

[0124] In summary, in the embodiment of the present invention, the server determines the target user, then obtains the application associated information of the target user, mines the associated users of the target user based on the application associated information, and then determines the application feature terms that match the associated users, and sends the application feature terms to the client of the target user; the client of the target user can receive the application feature terms sent by the server, and then obtain the input information of the target user, search for the target application feature terms that match the input information and display them; since the input difference between the target user and the corresponding associated users is small, the application feature terms corresponding to the associated users of the target user can better recommend information to the target user, thereby improving the input efficiency of the target user.

[0125] Secondly, in the embodiment of the present invention, in the process of determining the associated users of the target user, the server can obtain the application associated information of other users, and then convert the category information of the target user and each other user into corresponding category feature information, and determine the association information of the target user and each other user according to the category feature information; and then calculate the association degree of other users with the target user after converting the category feature information of the application into category feature information, so as to improve the accuracy of calculating the association degree. And for each user of the target user and other users: determine the application weight information corresponding to the user according to the usage information of the application corresponding to the user; perform weighted calculation on the category feature information corresponding to the user according to the application weight information corresponding to the user, and update the category feature information corresponding to the user; and then further improve the accuracy of calculating the association degree. Then, determine other users whose association information with the target user exceeds the association threshold as the associated users of the target user; and then send the application feature entries corresponding to the users with high association degree with the target user to the client of the target user, wherein the higher the association degree between the two users, the smaller the difference in input, so as to improve the accuracy of client information recommendation and further improve the efficiency of user input.

[0126] Furthermore, in the embodiment of the present invention, when mining application feature words, the server collects user terms entered by all network users in the application for an application, and the all network users include the target user and other users; for a user term, the number of users using the user term is counted, and the top M user terms with the highest number of users are determined as application feature terms of the application. Then, the high-frequency words of all network users in each application are used as application feature terms of the application; thereby improving the accuracy of client information recommendation and further improving user input efficiency.

[0127] Furthermore, in an embodiment of the present invention, when determining application feature entries that match each application with the associated user, the server determines, for each application feature entry of each application, the number of associated users using the application feature entry; the top N application feature entries with the highest number of associated users used are determined as application feature entries that match the application and the associated user; and then the application feature entries used by the majority of associated users are determined as application feature entries that match the associated user; thereby improving the accuracy of client information recommendations and further improving user input efficiency.

[0128] Furthermore, in an embodiment of the present invention, the client can respectively match the application identifier of each target application feature entry with the input environment information, determine the target application feature entry that matches the input environment, and then display the target application feature entry that matches the input environment information before other target application feature entries; and then display the application feature entry used by the associated user in the application currently located in the target user's client before the application feature entries used by the associated user in other applications, thereby enabling the target user to quickly select the required information and further improve input efficiency.

[0129] Again, in an embodiment of the present invention, the client can sort the feature terms of each target application according to the number of uses by the associated users, and then display the feature terms of each target application according to the sorting result; and thus, the application feature terms used by the majority of associated users are displayed before the application feature terms used by a minority of associated users, so that the target users can quickly select the required information, thereby further improving the input efficiency.

[0130] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0131] Reference Figure 4 , shows a structural block diagram of an embodiment of a server-side information recommendation device of the present invention, which may specifically include the following modules:

[0132] A user determination module 402, used to determine a target user;

[0133] A user mining module 404 is used to obtain application-related information of the target user and mine related users of the target user based on the application-related information;

[0134] The entry sending module 406 is used to determine the application feature entry that matches the associated user, and send the application feature entry to the client of the target user, so that the client of the target user can recommend information based on the application feature entry.

[0135] Reference Figure 5 , showing a structural block diagram of an optional embodiment of a server-side information recommendation device of the present invention.

[0136] In an optional embodiment of the present invention, the application association information includes application category information; the user mining module 404 is used to obtain application association information of other users, and the other users include multiple users; the category information of the target user and each other user is converted into corresponding category feature information; based on the category feature information, the association information between the target user and each other user is determined; other users whose association information with the target user exceeds the association threshold are determined as associated users of the target user.

[0137] In an optional embodiment of the present invention, the category feature information is an X-dimensional vector, X is a positive integer, X is equal to the sum of the application categories, and one dimension corresponds to one category of the application; the application association information also includes application usage information; and the device further includes:

[0138] The information update module 408 is used to determine the application weight information corresponding to the user based on the usage information of the application corresponding to the user for each of the target user and other users; perform weighted calculation on the category feature information corresponding to the user based on the application weight information corresponding to the user, and update the category feature information corresponding to the user.

[0139] In an optional embodiment of the present invention, the entry sending module 406 includes:

[0140] The term mining submodule 4062 is used to mine application feature terms of each application;

[0141] The term matching submodule 4064 is used to determine application feature terms that match each application with the associated user.

[0142] In an optional embodiment of the present invention, the term mining submodule 4062 is used to collect, for an application, user terms entered by users across the entire network in the application, wherein users across the entire network include the target user and other users; for a user term, count the number of users using the user term, and determine the top M user terms with the highest number of user usages as application feature terms of the application, where M is a positive integer.

[0143] In an optional embodiment of the present invention, the term matching submodule 4064 is used to determine the number of associated users using each application feature term of each application; the top N application feature terms with the highest number of associated users using are determined as the application feature terms that match the application and the associated users, where N is a positive integer.

[0144] In an optional embodiment of the present invention, the entry sending module 406 includes:

[0145] The feature entry sending submodule 4066 is used to configure the application identifier of the corresponding application for the application feature entry; and send the application feature entry with the application identifier and the corresponding associated user usage number to the client of the target user.

[0146] In summary, in an embodiment of the present invention, the server can determine the target user, then obtain the application associated information of the target user, and mine the associated users of the target user based on the application associated information; then determine the application feature terms that match the associated users, and send the application feature terms to the client of the target user, so that the client of the target user can recommend information based on the application feature terms; since the input difference between the target user and the corresponding associated users is small, based on the application feature terms corresponding to the associated users of the target user, better information recommendations can be made for the target user, thereby improving the input efficiency of the target user.

[0147] Reference Figure 6 , shows a structural block diagram of an embodiment of a client-side information recommendation device of the present invention, which may specifically include the following modules:

[0148] An entry receiving module 602 is used to receive an application feature entry sent by a server, wherein the application feature entry corresponds to an associated user of a target user;

[0149] The information acquisition module 604 is used to acquire the input information of the target user;

[0150] The recommendation module 606 is used to search for a target application feature term that matches the input information and display the target application feature term.

[0151] Reference Figure 7 , showing a structural block diagram of an optional embodiment of a client-side information recommendation device of the present invention.

[0152] In an optional embodiment of the present invention, the application feature entry has an application identifier, and the input information includes input environment information; the recommendation module 606 includes:

[0153] The first display submodule 6062 is used to match the application identifier of each target application feature entry with the input environment information, determine the target application feature entry that matches the input environment; and display the target application feature entry that matches the input environment information before other target application feature entries.

[0154] In an optional embodiment of the present invention, the entry receiving module 602 is used to receive the application feature entry and the associated user usage number corresponding to the application feature entry sent by the server;

[0155] The recommendation module 606 includes:

[0156] The second display submodule 6064 is used to sort the characteristic terms of each target application according to the usage number of the associated users; and to display the characteristic terms of each target application according to the sorting result.

[0157] In summary, in the embodiments of the present invention, the client of the target user receives the application feature terms sent by the server, and then after obtaining the input information of the target user, it can search for and display the target application feature terms that match the input information; wherein, the application feature terms correspond to the associated users of the target user. Since the input difference between the target user and the corresponding associated users is small, compared with the prior art that recommends information to users based on the system vocabulary, the embodiments of the present invention can better recommend information to the target user, thereby improving the input efficiency of the target user.

[0158] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0159] Figure 8 8 is a block diagram of a terminal device 800 for information recommendation according to an exemplary embodiment. For example, the terminal device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0160] Reference Figure 8 The terminal device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0161] The processing component 802 generally controls the overall operation of the terminal device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0162] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the terminal device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0163] The power component 806 provides power to various components of the terminal device 800. The power component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the terminal device 800.

[0164] The multimedia component 808 includes a screen that provides an output interface between the terminal device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the terminal device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0165] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the terminal device 800 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0166] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0167] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the terminal device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of the components, such as the display and keypad of the terminal device 800, and the sensor assembly 814 can also detect the position change of the terminal device 800 or a component of the terminal device 800, the presence or absence of contact between the user and the terminal device 800, the orientation or acceleration / deceleration of the terminal device 800, and the temperature change of the terminal device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0168] The communication component 816 is configured to facilitate the communication between the terminal device 800 and other devices in a wired or wireless manner. The terminal device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 814 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 814 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0169] In an exemplary embodiment, the terminal device 800 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0170] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of a terminal device 800 to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0171] A non-temporary computer-readable storage medium, when the instructions in the storage medium are executed by the processor of a terminal device, enables the terminal device to execute an information recommendation method, the method comprising: receiving an application feature entry sent by a server, the application feature entry corresponding to an associated user of a target user; obtaining input information of the target user; searching for a target application feature entry that matches the input information, and displaying the target application feature entry.

[0172] Optionally, the application feature entry has an application identifier, and the input information includes input environment information; the displaying of the target application feature entry includes: matching the application identifier of each target application feature entry with the input environment information respectively, and determining the target application feature entry that matches the input environment; and displaying the target application feature entry that matches the input environment information before other target application feature entries.

[0173] Optionally, the receiving of application feature terms sent by the server includes: receiving the application feature terms sent by the server and the number of associated users using the application feature terms; the display of the target application feature terms includes: sorting each target application feature term according to the number of associated users using the term; and displaying each target application feature term according to the sorting result.

[0174] Fig. 9 : is a structural diagram of a server 900 for information recommendation according to another exemplary embodiment of the present invention. The server may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPU) 922 (for example, one or more processors) and memory 932, and one or more storage media 930 (for example, one or more mass storage devices) storing application programs 942 or data 944. Among them, the memory 932 and the storage medium 930 can be short-term storage or permanent storage. The program stored in the storage medium 930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server. Furthermore, the central processing unit 922 can be configured to communicate with the storage medium 930 and execute a series of instruction operations in the storage medium 930 on the server.

[0175] The server may also include one or more power supplies 926, one or more wired or wireless network interfaces 950, one or more input and output interfaces 958, one or more keyboards 956, and / or one or more operating systems 941, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0176] A server includes a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors. The one or more programs include instructions for performing the following operations: determining a target user; obtaining application associated information of the target user, and mining associated users of the target user based on the application associated information; determining application feature terms that match the associated users, and sending the application feature terms to the client of the target user, so that the client of the target user recommends information based on the application feature terms.

[0177] Optionally, the application association information includes application category information; mining associated users of the target user based on the application association information includes: obtaining application association information of other users, the other users including multiple users; converting the category information of the target user and each other user into corresponding category feature information; determining the association degree information between the target user and each other user based on the category feature information; and determining other users whose association degree information with the target user exceeds an association degree threshold as associated users of the target user.

[0178] Optionally, the category characteristic information is an X-dimensional vector, where X is a positive integer, X is equal to the sum of the application categories, and one dimension corresponds to one category of application; the application association information also includes application usage information; and further includes instructions for performing the following operations: for each of the target user and other users: determining the application weight information corresponding to the user based on the application usage information corresponding to the user; performing weighted calculation on the category characteristic information corresponding to the user based on the application weight information corresponding to the user, and updating the category characteristic information corresponding to the user.

[0179] Optionally, the determining the application feature terms that match the associated user includes: mining application feature terms of each application; and determining the application feature terms that match each application and the associated user.

[0180] Optionally, the mining of associated terms of each application includes: for an application, collecting user terms entered by users from the entire network in the application, the users from the entire network including the target user and other users; for a user term, counting the number of users using the user term, and determining the top M user terms with the highest number of users using the user term as application feature terms of the application, where M is a positive integer.

[0181] Optionally, determining the application feature entries that match each application and the associated user includes: determining, for each application feature entry of each application, the number of associated users using the application feature entry; and determining top N application feature entries with the highest number of associated users using them as the application feature entries that match the application and the associated user, where N is a positive integer.

[0182] Optionally, sending the application feature entry to the client of the target user includes: configuring an application identifier of a corresponding application for the application feature entry; and sending the application feature entry with the application identifier and a corresponding number of associated user usages to the client of the target user.

[0183] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0184] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0185] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0186] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0187] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0188] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0189] The above is a detailed introduction to an information recommendation method, an information recommendation device and an electronic device provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An information recommendation method, characterized in that: include: The server determines the target user; Acquire application-related information of the target user, and mine related users of the target user based on the application-related information; The application-related information includes application category information and application usage information; Mining the associated terms of each application, wherein the mining of the associated terms of each application includes: for an application, collecting user terms input by users of the entire network in the application, wherein the users of the entire network include the target user and other users; for a user term, counting the number of users using the user term, and determining the first M user terms with the highest number of users using the user term as the application feature terms of the application, where M is a positive integer; For each application feature entry of each application, determining the number of users associated with the application feature entry; Determine the top N application feature terms with the highest usage count by the associated user as the application feature terms that match the application and the associated user, where N is a positive integer; Configuring an application identifier of a corresponding application for the application feature entry; The application feature entry with the application identifier and the corresponding associated user usage count are sent to the client of the target user, so that the client of the target user can make information recommendations based on the application feature entry.

2. The method according to claim 1, characterized in that The mining of associated users of the target user based on the application associated information includes: Acquire application-related information of other users, where the other users include multiple users; Respectively convert the category information of the target user and each other user into corresponding category feature information; Determining, based on the category characteristic information, the relevance information between the target user and each other user; Other users whose association information with the target user exceeds an association threshold are determined as associated users of the target user.

3. The method according to claim 2, characterized in that The category feature information is an X-dimensional vector, where X is a positive integer, X is equal to the sum of the application categories, and one dimension corresponds to one category of the application; the method further includes: For each of the target user and other users: Determine the weight information of the application corresponding to the user according to the usage information of the application corresponding to the user; The category characteristic information corresponding to the user is weighted and calculated according to the application weight information corresponding to the user, and the category characteristic information corresponding to the user is updated.

4. An information recommendation method, characterized in that: include: The client of the target user receives the application feature entry and the number of associated users using the application feature entry sent by the server; The application feature entry corresponds to an associated user of the target user, and the application feature entry has an application identifier; Acquire input information of the target user, wherein the input information includes input environment information; Searching for a target application feature entry that matches the input information, and displaying the target application feature entry; The displaying of the target application feature entry includes: matching the application identifier of each target application feature entry with the input environment information respectively, and determining the target application feature entry matching the input environment; Displaying the target application feature entry that matches the input environment information before other target application feature entries; The displaying of the target application feature entry also includes: sorting the characteristic terms of each target application program according to the usage number of the associated users; The characteristic terms of each target application are displayed according to the sorting results.

5. An information recommendation device, characterized in that: Applicable to servers, including: A user determination module, used to determine the target user; A user mining module is used to obtain application-related information of the target user, and mine users associated with the target user based on the application-related information; the application-related information includes application category information and application usage information; An entry sending module, used for determining an application feature entry matching the associated user, and sending the application feature entry to the client of the target user, so that the client of the target user can make information recommendations based on the application feature entry; The entry delivery module includes: The term mining submodule is used to mine the associated terms of each application, wherein the mining of the associated terms of each application includes: for an application, collecting user terms entered by users of the entire network in the application, wherein the users of the entire network include the target user and other users; for a user term, counting the number of users using the user term, and determining the first M user terms with the highest number of users using the user term as the application feature terms of the application, where M is a positive integer; The term matching submodule is used to determine the number of associated users using each application feature term of each application; the first N application feature terms with the highest number of associated users are determined as the application feature terms that match the application and the associated users, where N is a positive integer; The entry delivery module includes: The feature entry sending submodule is used to configure the application identifier of the corresponding application for the application feature entry; and send the application feature entry with the application identifier and the corresponding associated user usage number to the client of the target user.

6. The device according to claim 5, characterized in that The device also includes: The user mining module is used to obtain application association information of other users, including multiple other users; convert the category information of the target user and each other user into corresponding category feature information; determine the association information between the target user and each other user based on the category feature information; and determine other users whose association information with the target user exceeds an association threshold as associated users of the target user.

7. The device according to claim 6, characterized in that The category feature information is an X-dimensional vector, where X is a positive integer, X is equal to the sum of the application categories, and one dimension corresponds to one category of the application; the device further includes: The information updating module is used to determine the application weight information corresponding to the user based on the usage information of the application corresponding to the user for each of the target user and other users; perform weighted calculation on the category feature information corresponding to the user based on the application weight information corresponding to the user, and update the category feature information corresponding to the user.

8. An information recommendation device, characterized in that: Applied to the client, including: An entry receiving module, used for receiving an application feature entry and a usage number of an associated user corresponding to the application feature entry sent by a server, wherein the application feature entry corresponds to an associated user of a target user and has an application identifier; An information acquisition module, used to acquire input information of the target user, wherein the input information includes input environment information; A recommendation module, used to search for a target application feature entry that matches the input information and display the target application feature entry; Wherein, the recommendation module includes: The first display submodule is used to match the application identifier of each target application feature entry with the input environment information, determine the target application feature entry that matches the input environment; and display the target application feature entry that matches the input environment information before other target application feature entries; The second display submodule is used to sort the characteristic terms of each target application according to the usage number of the associated users; and to display the characteristic terms of each target application according to the sorting result.

9. A readable storage medium, characterized in that: When the instructions in the storage medium are executed by the processor of the server, the server is enabled to execute the information recommendation method as described in any one of method claims 1-3.

10. A readable storage medium, characterized in that: When the instructions in the storage medium are executed by the processor of the terminal device, the terminal device is enabled to execute the information recommendation method as described in method claim 4.

11. A server, characterized in that: It includes a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors. The one or more programs include instructions for performing the information recommendation method as described in any one of method claims 1-3.

12. A terminal device, characterized in that: The device comprises a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, wherein the one or more programs include instructions for performing the information recommendation method as claimed in claim 4.

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