CN113268655A - Information recommendation method and device and electronic equipment - Google Patents
Information recommendation method and device and electronic equipment Download PDFInfo
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Abstract
本发明实施例提供了一种信息推荐方法、装置和电子设备，其中，所述方法包括：确定目标用户和所述目标用户已安装的应用程序，以及确定所述目标用户对所述已安装的应用程序的兴趣度，其中，所述已安装应用程序为除去预安装的应用程序之外的应用程序；依据所述目标用户对所述已安装的应用程序的兴趣度，确定所述目标用户对未安装的应用程序的兴趣度；依据所述目标用户对所述已安装的应用程序的兴趣度和所述未安装的应用程序的兴趣度，为所述目标用户进行信息推荐；进而解决现有技术由于没有历史数据而很难确定用户兴趣信息导致信息推荐准确性差的问题，提高了为用户进行信息推荐的准确性。
Embodiments of the present invention provide an information recommendation method, apparatus, and electronic device, wherein the method includes: determining a target user and an application program installed by the target user, and determining the target user's preference for the installed application. The interest degree of the application, wherein the installed application is an application except the pre-installed application; according to the interest degree of the target user in the installed application, determine the target user's interest in the installed application The interest degree of the application program that is not installed; according to the interest degree of the target user in the installed application program and the interest degree of the application program not installed, perform information recommendation for the target user; and then solve the problem of existing Due to the lack of historical data, it is difficult to determine the user's interest information, which leads to the problem of poor information recommendation accuracy, which improves the accuracy of information recommendation for users.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an information recommendation method and apparatus, and an electronic device.
Background
Currently, each large platform generally recommends information for users according to user interests in order to acquire more customers. However, with the increasing market competitiveness, the cost of acquiring customers is higher and higher, and how to effectively analyze the interests of users by using derivative data becomes one of the problems to be solved by each platform.
In the prior art, each platform mainly relies on the historical behaviors of the user in the platform, such as clicking behaviors, browsing behaviors and the like, to analyze the interests of the user. For a new user of the platform, because the new user does not have any historical behavior on the platform, the platform is difficult to analyze the interest of the user and cannot accurately recommend information for the user.
Disclosure of Invention
The embodiment of the invention provides an information recommendation method, which aims to improve the accuracy of information recommendation.
Correspondingly, the embodiment of the invention also provides an information recommendation device and electronic equipment, which are used for ensuring the realization and application of the method.
In order to solve the above problem, an embodiment of the present invention discloses an information recommendation method, which specifically includes: determining a target user and an installed application program of the target user, and determining the interest degree of the target user in the installed application program, wherein the installed application program is an application program except a pre-installed application program; determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program; and recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
Optionally, the determining the interest level of the target user in the installed application includes: acquiring the use information of the installed application program; and determining the interest degree of the target user in the installed application program according to the use information.
Optionally, the determining the interest level of the target user in the uninstalled application according to the interest level of the target user in the installed application includes at least one of: determining the interest degree of the target user in the uninstalled application program by adopting a collaborative filtering algorithm according to the interest degree of the target user in the installed application program; determining the interest degree of the target user in the uninstalled application program by adopting a matrix decomposition algorithm according to the interest degree of the target user in the installed application program; and determining the interest degree of the target user in the uninstalled application program by adopting a neural collaborative filtering algorithm according to the interest degree of the target user in the installed application program.
Optionally, the recommending information for the target user according to the interest-degree of the target user in the installed application and the interest-degree of the target user in the uninstalled application includes: determining interest categories of application programs according to a preset mapping relation, wherein the application programs comprise installed application programs and uninstalled application programs; aiming at each interest category, calculating the interest degree of the target user in the interest category by adopting the interest degree of the target user in the application program belonging to the interest category; and recommending information for the target user according to the interestingness of each interest category.
Optionally, the recommending information for the target user according to the interest level of each interest category includes: determining the top N interest categories with highest interest degree; and recommending information corresponding to the previous N interest categories for the target user.
The embodiment of the invention also discloses an information recommendation device, which specifically comprises: the first information determination module is used for determining a target user and an installed application program of the target user and determining the interest degree of the target user in the installed application program, wherein the installed application program is an application program except a pre-installed application program; the second information determining module is used for determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program; and the recommending module is used for recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
Optionally, the first information determining module is configured to obtain usage information of the installed application program; and determining the interest degree of the target user in the installed application program according to the use information.
Optionally, the second information determining module includes: a first interest-degree determining submodule, configured to determine, by using a collaborative filtering algorithm, a target user's interest degree in an uninstalled application program according to the target user's interest degree in the installed application program; the second interestingness determining submodule is used for determining the interestingness of the target user on the uninstalled application program according to the interestingness of the target user on the installed application program by adopting a matrix decomposition algorithm; and the third interestingness determining submodule is used for determining the interestingness of the target user on the uninstalled application program according to the interestingness of the target user on the installed application program by adopting a neural collaborative filtering algorithm.
Optionally, the recommendation module includes: the category determination submodule is used for determining interest categories to which the application programs belong according to a preset mapping relation, wherein the application programs comprise installed application programs and uninstalled application programs; the category interest degree determining sub-module is used for calculating the interest degree of the target user in the interest categories by adopting the interest degree of the target user in the application programs belonging to the interest categories according to each interest category; and the information recommendation submodule is used for recommending information for the target user according to the interest degree of each interest category.
Optionally, the information recommendation sub-module is configured to determine the top N interest categories with the highest interest level; and recommending information corresponding to the previous N interest categories for the target user.
The embodiment of the invention also discloses a readable storage medium, and when instructions in the storage medium are executed by a processor of the electronic equipment, the electronic equipment can execute the information recommendation method in any one of the embodiments of the invention.
An embodiment of the present invention also discloses an electronic device, including a memory, and one or more programs, where the one or more programs are stored in the memory, and configured to be executed by one or more processors, and the one or more programs include instructions for: determining a target user and an installed application program of the target user, and determining the interest degree of the target user in the installed application program, wherein the installed application program is an application program except a pre-installed application program; determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program; and recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
Optionally, the determining the interest level of the target user in the installed application includes: acquiring the use information of the installed application program; and determining the interest degree of the target user in the installed application program according to the use information.
Optionally, the determining the interest level of the target user in the uninstalled application according to the interest level of the target user in the installed application includes at least one of: determining the interest degree of the target user in the uninstalled application program by adopting a collaborative filtering algorithm according to the interest degree of the target user in the installed application program; determining the interest degree of the target user in the uninstalled application program by adopting a matrix decomposition algorithm according to the interest degree of the target user in the installed application program; and determining the interest degree of the target user in the uninstalled application program by adopting a neural collaborative filtering algorithm according to the interest degree of the target user in the installed application program.
Optionally, the recommending information for the target user according to the interest-degree of the target user in the installed application and the interest-degree of the target user in the uninstalled application includes: determining interest categories of application programs according to a preset mapping relation, wherein the application programs comprise installed application programs and uninstalled application programs; aiming at each interest category, calculating the interest degree of the target user in the interest category by adopting the interest degree of the target user in the application program belonging to the interest category; and recommending information for the target user according to the interestingness of each interest category.
Optionally, the recommending information for the target user according to the interest level of each interest category includes: determining the top N interest categories with highest interest degree; and recommending information corresponding to the previous N interest categories for the target user.
The embodiment of the invention has the following advantages:
in the embodiment of the invention, a target user, an installed application program of the target user and the interest degree of the target user in the installed application program can be determined firstly; then, according to the interest degree of the target user in the installed application program, determining the interest degree of the target user in the uninstalled application program; information recommendation is carried out on the target user according to the interest degree of the target user in the installed application program and the interest degree of the non-installed application program; the problem that information recommendation accuracy is poor due to the fact that the user interest information is difficult to determine due to the fact that historical data does not exist in the prior art is solved, and accuracy of information recommendation for the user is improved.
Drawings
FIG. 1 is a flow chart of the steps of an embodiment of a method for information recommendation of the present invention;
FIG. 2 is a flow chart of the steps of an alternative embodiment of an information recommendation method of the present invention;
FIG. 3 is a block diagram of an embodiment of an information recommendation device according to the present invention;
FIG. 4 is a block diagram of an alternative embodiment of an information recommendation device of the present invention;
FIG. 5 illustrates a block diagram of an electronic device for information recommendation, according to an example embodiment;
fig. 6 is a schematic structural diagram of an electronic device for information recommendation according to another exemplary embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
One of the core ideas of the embodiment of the invention is that based on the interests of the installed application programs of the user, the interests of the user on the uninstalled application programs are determined; then information recommendation is carried out for the user by combining the interests of the user on the installed application programs and the uninstalled application programs; furthermore, the embodiment of the invention solves the problem of poor information recommendation accuracy caused by the fact that the user interest is difficult to determine due to the absence of historical behavior data in the prior art, and improves the accuracy of information recommendation for the user.
Referring to fig. 1, a flowchart illustrating steps of an embodiment of an information recommendation method according to the present invention is shown, which may specifically include the following steps:
And step 104, determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program.
And 106, recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
In the embodiment of the invention, the user needing information recommendation can be determined as the target user. Because the application program installed on the terminal device corresponding to the target user can reflect the interest of the target user, the embodiment of the invention can acquire the application program installation list on the terminal device corresponding to the target user and determine the installed application program on the terminal device corresponding to the target user. The application programs installed in the terminal equipment comprise pre-installed application programs and application programs actively installed by a user; the pre-installed application may refer to an application installed when the terminal device leaves a factory. Since the pre-installed application is not actively installed by the user and the pre-installed applications of many users are the same, the user's interest cannot be reflected. In order to improve the accuracy of determining interest information corresponding to a target user, an installed application determined by the embodiment of the present invention may refer to an application other than a pre-installed application.
In the embodiment of the invention, the interest degree of the target user in the installed application program can be analyzed according to the use information of the target user in the installed application program; and then recommending the user information according to the interest degree of the target user in the installed application program.
In an embodiment of the present invention, the interest level of the target user in the uninstalled application program may be determined according to the interest level of the target user in the installed application program; and then information recommendation is carried out for the user by combining the interest degree of the target user on the installed application program and the interest degree of the uninstalled application program. And further, information recommendation is accurately performed on the user under the condition that no target user historical behavior data exists. The information recommended for the target user may include multiple types, such as pictures, texts, videos, animations, and the like, which is not limited in this embodiment of the present invention.
In summary, in the embodiment of the present invention, a target user, an installed application of the target user, and a level of interest of the target user in the installed application may be determined first; then, according to the interest degree of the target user in the installed application program, determining the interest degree of the target user in the uninstalled application program; information recommendation is carried out on the target user according to the interest degree of the target user in the installed application program and the interest degree of the non-installed application program; the problem that information recommendation accuracy is poor due to the fact that the user interest is difficult to determine due to the fact that historical data does not exist in the prior art is solved, and accuracy of information recommendation for the user is improved.
Referring to fig. 2, a flowchart illustrating steps of an alternative embodiment of the information recommendation method of the present invention is shown, which may specifically include the following steps:
In an example of the present invention, the obtained application installation list of the terminal device corresponding to the target user may be as follows:
TABLE 1
Wherein, each item in the application program installation list is the package name of the application program. From table 1, it can be determined that the number of applications installed by the target user is 7.
And step 204, determining the interest degree of the target user in the installed application program.
In an example of the present invention, one way to determine the interest level of the target user in the installed application program may be to use a first preset value as the interest level of the target user in the installed application program; the first preset value can be set as required, such as 1; the embodiments of the present invention are not limited in this regard.
As an example of the present invention, the first preset value is 1, and the interestingness of each installed application in table 1 can be shown in table 2:
| application program list | Degree of interest |
| com.abc.momo | 1 |
| com.asssd.bangbang | 1 |
| com.ddfgg.newsclient | 1 |
| com.tggbnh.news | 1 |
| com.hffrj.mybus | 1 |
| com.jiaj | 1 |
| com.suhsuhiuhui.haofenshu | 1 |
TABLE 2
Of course, in another example of the present invention, in order to improve the accuracy of determining the interest information of the target user, a manner of determining the interest degree of the target user in the installed application program may include the following sub-steps:
substep 22, obtaining the use information of the installed application program;
and a substep 24 of determining the interest level of the target user in the installed application program according to the use information.
In this embodiment of the present invention, the usage information may refer to information that the target user uses the installed application, such as frequency of use, duration of use, date of use, and the like, which is not limited in this embodiment of the present invention. Then, for each installed application program, the interest level of the target user in the installed application program can be determined according to the use information of the installed application program. When the usage information of the installed application program contains a plurality of items of information, a corresponding weight may be set for each item of information in advance, and then the interest level of the target user in the installed application program is determined by performing weighted calculation on each item of information in the usage information. Wherein the greater the interest level, the greater the interest of the characterization target user in the installed application program.
As an example of the present invention, the interest level of the target user in the installed application program can be determined by referring to the following formula:
wherein g (k) represents the interest level of the target user in the installed application k. Δ d represents the number of days between the last use of application k and the current time. numall represents the total number of applications that the target user has installed. The count indicates the usage frequency of the application k by the target user. counter represents the total usage frequency of all installed applications by the target user. usertime represents the usage duration of the application k by the target user. usertimeall represents the total usage time of all applications installed by the target user.
For example, numall is 15, countall is 700, and usertimeall is 5000. If the obtained use information corresponding to the installed application program (com.abc.momo) by the target user is: use frequency count 100, use duration usertime 500 hours, use date 2019.11.10 (last use date). The usage information corresponding to the installed application program (com.assd.bangbang) by the target user is as follows: use frequency count 200, use duration usertime 700 hours, use date 2019.11.01 (last use date). If the current date is 2019.12.23, according to the above formula, the interest level of the target user in the installed application program (com.abc.momo) is obtained as follows: 0.007369, respectively; obtaining the interest degree of the target user in the installed application program (com.assd.bangbang) as: 0.007135.
as an example of the present invention, the calculated interestingness for each installed application in Table 1 using substeps 22-24 may be as shown in Table 3:
| application program list | Degree of interest |
| com.abc.momo | 0.007369 |
| com.asssd.bangbang | 0.007135 |
| com.ddfgg.newsclient | 0.006001 |
| com.tggbnh.news | 0.005021 |
| com.hffrj.mybus | 0.009136 |
| com.jiaj | 0.008335 |
| com.suhsuhiuhui.haofenshu | 0.007996 |
TABLE 3
And step 206, determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program.
Similarly, the embodiment of the present invention may determine the interest level of the target user in the uninstalled application program, and characterize the interest level of the target user in the uninstalled application program by using the interest level of the target user in the uninstalled application program.
In an embodiment of the present invention, a manner of determining the interest level of the target user in the uninstalled application program according to the interest level of the target user in the installed application program may be that a collaborative filtering algorithm is used to determine the interest level of the target user in the uninstalled application program according to the interest level of the target user in the installed application program. Determining the interest degree of a target user in an uninstalled application program by adopting a user-based collaborative filtering algorithm according to the interest degree of the target user in the installed application program; or a collaborative filtering algorithm based on an article (which may refer to an application program in the embodiment of the present invention) may be adopted, and the interest level of the target user in the uninstalled application program is determined according to the interest level of the target user in the installed application program, which is not limited in the embodiment of the present invention.
In one example of the present invention, the collaborative filtering algorithm based on users may refer to the following expression:
wherein p (u, i) represents the interest degree of the target user (marked by u) in the uninstalled application program i; s (u, K) comprises K other users with the most similar interests to the target user u, wherein the other users are users except the target user in the whole network users, N (i) is a user set corresponding to the installed application program i in the K other users, and W (i) is a user set corresponding to the installed application program i in the K other usersuvIs the target user u and other users (using v-marks)Identification) similarity of interest, RviRepresenting the interest level of other users v in the installed application i.
The manner of determining the interest level of the other user in the installed application program is similar to the manner of determining the interest level of the target user in the installed application program in step 204, and is not described herein again. The determination of a set of associated users S (u, K) with similar interests to the target user will now be described. Wherein, a null vector with X-dimension can be established for each user in the full-network users. Wherein, X is the total of the types of the application programs installed by the full-network users, and each dimension of the null vector corresponds to one type of the application programs installed by the full-network users. Adding the interestingness of each application program installed by the user to the corresponding dimensionality of the null vector; setting a value corresponding to the dimension without the added interest degree in the empty vector as a second preset value; and obtaining the interestingness vector of each user corresponding to the installed application program. The second preset value may be set to be 0 as required, which is not limited in this embodiment of the present invention. Then, the interest degree vectors corresponding to other users in the whole network users and the interest similarity of the interest degree vectors corresponding to the target user can be respectively calculated; and generating an associated user set S (u, K) by adopting the first K users with the highest interest similarity.
In one example of the present invention, the expression of the article-based collaborative filtering algorithm may be as follows:
wherein q (u, m) represents the interest degree of the target user (using u identification) in the uninstalled application program m; n (u) represents a set of installed applications of the target user u (n represents one of the installed applications), S (n, K) is a set of applications including K other applications most similar to the installed applications n (m is an uninstalled application of one of the target users u in the set); wmnIs a similarity of an uninstalled application m and an installed application nDegree, RunRepresenting the interest level of the target user u in the installed application n.
The determination of the similarity to the set S (n, K), the determination of the non-installed application m and the installed application n will now be described. The number of first users for installing the application x and the number of second users for installing the application y may be counted based on the applications installed by the network-wide users, and the number of third users for installing the application x and the application y at the same time may be counted. Then, the similarity between the application program x and the application program y is calculated according to the first user number, the second user number and the third user number. The following expression may be referred to:
wherein, n (x) is the first user number, n (y) is the second user number, and | n (x) & n (y) | is the third user number. Further, according to the formula, the similarity between the uninstalled application program m and the installed application program n can be determined; and the first K applications with the highest similarity may be used to form the set S (n, K).
In an embodiment of the present invention, a manner of determining the interest level of the target user in the uninstalled application program according to the interest level of the target user in the installed application program may be that a matrix decomposition algorithm is used to determine the interest level of the target user in the uninstalled application program according to the interest level of the target user in the installed application program. The matrix Z of A x B can be generated by adopting the interestingness of each user in the full-network users to the application program (including the interestingness to the installed application program and the interestingness to the uninstalled application program; the interestingness to the uninstalled application program can be an unknown number f), wherein A is the number of the full-network users, and B is the sum of the types of the installed application programs corresponding to the full-network users. Any element Z in the matrixabThe interest level of the user a in the application program b. The matrix Z may then be decomposed (e.g., using an alternating least squares method) into two matrices Z1 and Z2; z1 is A C, Z2 is B C, and Z is the product of the transpose of Z1 and Z2And (4) accumulating. The transposed points of Z1 and Z2 can be multiplied to obtain the interest level of the target user in the uninstalled application program.
In an embodiment of the present invention, a manner of determining the interest level of the target user in the uninstalled application program according to the interest level of the target user in the installed application program may be that a neural collaborative filtering algorithm is used to determine the interest level of the target user in the uninstalled application program according to the interest level of the target user in the installed application program. The target user and the installed application program corresponding to the target user may be encoded, for example, (one-hot) encoding) to obtain a feature vector corresponding to the target user and a feature vector of the installed application program corresponding to the target user. And then inputting the feature vector corresponding to the target user and the feature vector of the installed application program corresponding to the target user into the trained neural network model, and predicting the interest degree of the target user on the uninstalled application program by using the neural network model.
Of course, other ways of determining the interest level of the target user in the uninstalled application program may also be included, and the embodiment of the present invention is not limited thereto.
As an example of the present invention, according to the interest level of the target user in each installed application in table 3, the determined interest level of the target user in the uninstalled application is shown in table 4:
| application program list | Degree of interest |
| com.agg.next | 0.008995 |
| org.xinkb.blackboard.android | 0.006733 |
| com.baanana.homework | 0.005636 |
TABLE 4
And 208, determining interest categories of the installed application programs and the uninstalled application programs corresponding to the target user according to a preset mapping relation.
And step 210, calculating the interestingness of the corresponding interest category by adopting the interestingness of the installed application programs and/or the uninstalled application programs with the same interest category.
In the embodiment of the invention, the application programs installed by the users in the whole network can be divided in advance to obtain a plurality of interest categories; and establishing a mapping relation between each application program and the corresponding interest category. The manner of dividing the application category may include multiple manners, such as dividing according to the function of the application, dividing according to the user group of the application, and the like, which is not limited in this embodiment of the present invention.
In the embodiment of the present invention, the interest category to which the application installed by the target user belongs may be determined according to a preset mapping relationship, and the interest category to which the application not installed by the target user belongs may be determined. Wherein each interest category may only include applications that the target user has installed; or may include only applications not installed by the target user; and the method can also simultaneously comprise the installed application programs and the uninstalled application programs of the target user. And calculating the average value of the interest degree of the target user to the application program belonging to the interest category aiming at each interest category to obtain the interest degree of the target user to the interest category. As an example, the target user's interest level in each interest category may be determined with reference to the following expression:
wherein G ishThe interest degree of the target user to the h interest category is obtained; numhThe number of applications contained for the h-th interest category; apppAnd representing the interest degree of the target user to the p & ltth & gt application program in the h & ltth & gt interest category.
As an example of the present invention, the applications that the target user has installed are shown in Table 3, and the uninstalled applications are shown in Table 4. The interest categories to which the installed applications in table 3 and the uninstalled applications in table 4 belong are determined based on the mapping relationship as follows: abc, mo, social chat, com, ass, bangban, com, suhsuhiuhui, haofenshu, org, xinkb, blackberry, andrrid, com, baanana, homework, education, com, ddfgg, news, com, tggbnh, news, com, agg, next, news, mybus, shopping, com, jiaj. Then, according to the interestingness in tables 3 and 4, the interestingness corresponding to each interest category can be calculated as: interest degree corresponding to social chat class: 0.007369, interest level corresponding to primary and secondary school education: 0.006875, interest level corresponding to news reading class: 0.006672, interest level corresponding to the travel category: 0.009136, interest level corresponding to shopping category: 0.008335. and further obtaining interest information of the target user, such as: { interest level corresponding to social chat class: 0.007369, interestingness 0.006875 for primary and middle school education, interestingness for news reading: 0.006672, interest level corresponding to the travel category: 0.009136, interest level corresponding to shopping category: 0.008335}.
And step 212, recommending information for the target user according to the interestingness of each interest category.
In the embodiment of the present invention, a method for recommending information for the target user based on the interest level of each interest category may include the following substeps 42-44:
and a substep 42 of determining the top N interest categories with the highest interest level.
And a substep 44 of recommending information corresponding to the top N interest categories for the target user.
In an optional embodiment of the present invention, the top N interest categories with the highest interest degree may be selected from the interest information of the target user; n may be set as required, which is not limited in this embodiment of the present invention. For example, if N is 3, the social chat class, the transportation class, and the shopping class may be selected from the determined interest information. Information associated with the top N interest categories, such as videos, pictures, etc., is then recommended for the target user.
Certainly, other ways of recommending information for the target user according to the interestingness of each interest category may also be included, such as determining the interest category of which the interestingness is higher than the interestingness threshold in the interest information, and recommending information corresponding to the interest category of which the interestingness is higher than the interestingness threshold for the target user; the interestingness threshold value can be set according to requirements; the embodiments of the present invention are not limited in this regard.
In summary, in the embodiment of the present invention, the use information of the installed application program is obtained, and then the interest level of the target user in the installed application program is determined according to the use information; the accuracy of determining the interest level of the target user in the installed application program can be improved.
Further, in the embodiment of the present invention, according to a preset mapping relationship, an interest category to which each application program belongs is determined, then, for each interest category, the interest level of the target user in the application program belonging to the interest category is calculated by using the interest level of the target user in the application program belonging to the interest category, and then, information recommendation is performed for the user according to the interest level of each interest category; and then, the interest degree of the user to different interest categories can be determined, and the granularity of determining the interest of the user is increased, so that finer-grained information recommendation can be performed for the user, and the accuracy of information recommendation is improved.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Referring to fig. 3, a block diagram of an embodiment of an information recommendation apparatus according to the present invention is shown, which may specifically include the following modules:
a first information determination module 302, configured to determine a target user and an installed application of the target user, and determine a level of interest of the target user in the installed application, where the installed application is an application other than a pre-installed application; (ii) a
A second information determining module 304, configured to determine, according to the interest level of the target user in the installed application program, an interest level of the target user in an uninstalled application program;
and the recommending module 306 is configured to recommend information to the target user according to the interest level of the target user in the installed application and the interest level of the target user in the uninstalled application.
Referring to fig. 4, a block diagram of an alternative embodiment of an information recommendation device of the present invention is shown.
In an optional embodiment of the present invention, the first information determining module 302 is configured to obtain usage information of the installed application program; and determining the interest degree of the target user in the installed application program according to the use information.
In an optional embodiment of the present invention, the second information determining module 304 includes:
a first interest-degree determining submodule 3042, configured to determine, by using a collaborative filtering algorithm, a target user's interest degree in an uninstalled application program according to the target user's interest degree in the installed application program;
a second interest-degree determining submodule 3044, configured to determine, by using a matrix decomposition algorithm, a degree of interest of the target user in an uninstalled application program according to the degree of interest of the target user in the installed application program;
a third interestingness determining submodule 3046, configured to determine, by using a neural collaborative filtering algorithm, the interestingness of the target user in the uninstalled application program according to the interestingness of the target user in the installed application program.
In an optional embodiment of the present invention, the recommending module 306 includes:
the category determination submodule 3062 is configured to determine interest categories to which the application programs belong according to a preset mapping relationship, where the application programs include installed application programs and uninstalled application programs;
the category interestingness determination submodule 3064 is configured to, for each interest category, calculate the interestingness of the target user in the interest category by using the interestingness of the target user in the application program belonging to the interest category;
the information recommendation submodule 3066 is configured to recommend information to the target user according to the interest level of each interest category.
In an optional embodiment of the present invention, the information recommending submodule 3066 is configured to determine the top N interest categories with the highest interest degree; and recommending information corresponding to the previous N interest categories for the target user.
In summary, in the embodiment of the present invention, a target user, an installed application of the target user, and a level of interest of the target user in the installed application may be determined first; then, according to the interest degree of the target user in the installed application program, determining the interest degree of the target user in the uninstalled application program; information recommendation is carried out on the target user according to the interest degree of the target user in the installed application program and the interest degree of the non-installed application program; the problem that information recommendation accuracy is poor due to the fact that the user interest is difficult to determine due to the fact that historical data does not exist in the prior art is solved, and accuracy of information recommendation for the user is improved.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
Fig. 5 is a block diagram illustrating an electronic device 500 for information recommendation according to an example embodiment. For example, the electronic device 500 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 5, electronic device 500 may include one or more of the following components: a processing component 502, a memory 504, a power component 506, a multimedia component 508, an audio component 510, an input/output (I/O) interface 512, a sensor component 514, and a communication component 516.
The processing component 502 generally controls overall operation of the electronic device 500, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing elements 502 may include one or more processors 520 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 502 can include one or more modules that facilitate interaction between the processing component 502 and other components. For example, the processing component 502 can include a multimedia module to facilitate interaction between the multimedia component 508 and the processing component 502.
The memory 504 is configured to store various types of data to support operation at the device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 504 may be implemented by any type or combination of volatile or non-volatile memory devices 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 or optical disks.
The power component 506 provides power to the various components of the electronic device 500. Power components 506 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for electronic device 500.
The multimedia component 508 includes a screen that provides an output interface between the electronic device 500 and a 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 an input signal from a 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 boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 508 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 500 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 510 is configured to output and/or input audio signals. For example, the audio component 510 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 500 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 504 or transmitted via the communication component 516. In some embodiments, audio component 510 further includes a speaker for outputting audio signals.
The I/O interface 512 provides an interface between the processing component 502 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 514 includes one or more sensors for providing various aspects of status assessment for the electronic device 500. For example, the sensor assembly 514 may detect an open/closed state of the device 500, the relative positioning of components, such as a display and keypad of the electronic device 500, the sensor assembly 514 may detect a change in the position of the electronic device 500 or a component of the electronic device 500, the presence or absence of user contact with the electronic device 500, orientation or acceleration/deceleration of the electronic device 500, and a change in the temperature of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 516 is configured to facilitate wired or wireless communication between the electronic device 500 and other devices. The electronic device 500 may 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 section 514 receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communications component 514 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 500 may 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, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 504 comprising instructions, executable by the processor 520 of the electronic device 500 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
A non-transitory computer readable storage medium in which instructions, when executed by a processor of an electronic device, enable the electronic device to perform a method of information recommendation, the method comprising: determining a target user and an installed application program of the target user, and determining the interest degree of the target user in the installed application program, wherein the installed application program is an application program except a pre-installed application program; determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program; and recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
Optionally, the determining the interest level of the target user in the installed application includes: acquiring the use information of the installed application program; and determining the interest degree of the target user in the installed application program according to the use information.
Optionally, the determining the interest level of the target user in the uninstalled application according to the interest level of the target user in the installed application includes at least one of: determining the interest degree of the target user in the uninstalled application program by adopting a collaborative filtering algorithm according to the interest degree of the target user in the installed application program; determining the interest degree of the target user in the uninstalled application program by adopting a matrix decomposition algorithm according to the interest degree of the target user in the installed application program; and determining the interest degree of the target user in the uninstalled application program by adopting a neural collaborative filtering algorithm according to the interest degree of the target user in the installed application program.
Optionally, the recommending information for the target user according to the interest-degree of the target user in the installed application and the interest-degree of the target user in the uninstalled application includes: determining interest categories of application programs according to a preset mapping relation, wherein the application programs comprise installed application programs and uninstalled application programs; aiming at each interest category, calculating the interest degree of the target user in the interest category by adopting the interest degree of the target user in the application program belonging to the interest category; and recommending information for the target user according to the interestingness of each interest category.
Optionally, the recommending information for the target user according to the interest level of each interest category includes: determining the top N interest categories with highest interest degree; and recommending information corresponding to the previous N interest categories for the target user.
Fig. 6 is a schematic structural diagram of an electronic device 600 for information recommendation according to another exemplary embodiment of the present invention. The electronic device 600 may be a server, which may vary greatly due to different configurations or capabilities, and may include one or more Central Processing Units (CPUs) 622 (e.g., one or more processors) and memory 632, one or more storage media 630 (e.g., one or more mass storage devices) storing applications 642 or data 644. Memory 632 and storage medium 630 may be, among other things, transient or persistent storage. The program stored in the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations for the server. Still further, the central processor 622 may be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the server.
The server may also include one or more power supplies 626, one or more wired or wireless network interfaces 650, one or more input-output interfaces 658, one or more keyboards 656, and/or one or more operating systems 641, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.
An electronic device comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors the one or more programs including instructions for: determining a target user and an installed application program of the target user, and determining the interest degree of the target user in the installed application program, wherein the installed application program is an application program except a pre-installed application program; determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program; and recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
Optionally, the determining the interest level of the target user in the installed application includes: acquiring the use information of the installed application program; and determining the interest degree of the target user in the installed application program according to the use information.
Optionally, the determining the interest level of the target user in the uninstalled application according to the interest level of the target user in the installed application includes at least one of: determining the interest degree of the target user in the uninstalled application program by adopting a collaborative filtering algorithm according to the interest degree of the target user in the installed application program; determining the interest degree of the target user in the uninstalled application program by adopting a matrix decomposition algorithm according to the interest degree of the target user in the installed application program; and determining the interest degree of the target user in the uninstalled application program by adopting a neural collaborative filtering algorithm according to the interest degree of the target user in the installed application program.
Optionally, the recommending information for the target user according to the interest-degree of the target user in the installed application and the interest-degree of the target user in the uninstalled application includes: determining interest categories of application programs according to a preset mapping relation, wherein the application programs comprise installed application programs and uninstalled application programs; aiming at each interest category, calculating the interest degree of the target user in the interest category by adopting the interest degree of the target user in the application program belonging to the interest category; and recommending information for the target user according to the interestingness of each interest category.
Optionally, the recommending information for the target user according to the interest level of each interest category includes: determining the top N interest categories with highest interest degree; and recommending information corresponding to the previous N interest categories for the target user.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The information recommendation method, the information recommendation device and the electronic device provided by the invention are described in detail, and specific examples are applied in the text to explain the principle and the implementation of the invention, and the description of the above embodiments is only used to help understanding the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. An information recommendation method, comprising:
determining a target user and an installed application program of the target user, and determining the interest degree of the target user in the installed application program, wherein the installed application program is an application program except a pre-installed application program;
determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program;
and recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
2. The method of claim 1, wherein determining the target user's interest level in the installed application comprises:
acquiring the use information of the installed application program;
and determining the interest degree of the target user in the installed application program according to the use information.
3. The method of claim 1, wherein determining the target user's interest level in the uninstalled application based on the target user's interest level in the installed application comprises at least one of:
determining the interest degree of the target user in the uninstalled application program by adopting a collaborative filtering algorithm according to the interest degree of the target user in the installed application program;
determining the interest degree of the target user in the uninstalled application program by adopting a matrix decomposition algorithm according to the interest degree of the target user in the installed application program;
and determining the interest degree of the target user in the uninstalled application program by adopting a neural collaborative filtering algorithm according to the interest degree of the target user in the installed application program.
4. The method according to claim 1, wherein the recommending information for the target user according to the interest level of the target user in the installed application and the interest level of the target user in the uninstalled application comprises:
determining interest categories of application programs according to a preset mapping relation, wherein the application programs comprise installed application programs and uninstalled application programs;
aiming at each interest category, calculating the interest degree of the target user in the interest category by adopting the interest degree of the target user in the application program belonging to the interest category;
and recommending information for the target user according to the interestingness of each interest category.
5. The method according to claim 4, wherein the recommending information for the target user according to the interest level of each interest category comprises:
determining the top N interest categories with highest interest degree;
and recommending information corresponding to the previous N interest categories for the target user.
6. An information recommendation apparatus, comprising:
the first information determination module is used for determining a target user and an installed application program of the target user and determining the interest degree of the target user in the installed application program, wherein the installed application program is an application program except a pre-installed application program;
the second information determining module is used for determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program;
and the recommending module is used for recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
7. The apparatus of claim 6,
the first information determining module is used for acquiring the use information of the installed application program; and determining the interest degree of the target user in the installed application program according to the use information.
8. The apparatus of claim 6, wherein the second information determining module comprises:
a first interest-degree determining submodule, configured to determine, by using a collaborative filtering algorithm, a target user's interest degree in an uninstalled application program according to the target user's interest degree in the installed application program;
the second interestingness determining submodule is used for determining the interestingness of the target user on the uninstalled application program according to the interestingness of the target user on the installed application program by adopting a matrix decomposition algorithm;
and the third interestingness determining submodule is used for determining the interestingness of the target user on the uninstalled application program according to the interestingness of the target user on the installed application program by adopting a neural collaborative filtering algorithm.
9. An electronic device comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors the one or more programs including instructions for:
determining a target user and an installed application program of the target user, and determining the interest degree of the target user in the installed application program, wherein the installed application program is an application program except a pre-installed application program;
determining the interest degree of the target user in the uninstalled application program according to the interest degree of the target user in the installed application program;
and recommending information for the target user according to the interest degree of the target user in the installed application program and the interest degree of the uninstalled application program.
10. A readable storage medium, wherein instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the information recommendation method of any of method claims 1-5.
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| CN117390292A (en) * | 2023-12-12 | 2024-01-12 | 深圳格隆汇信息科技有限公司 | Application program information recommendation method, system and equipment based on machine learning |
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