WO2021196435A1 - Information recommendation method and related device - Google Patents

Abstract

An information recommendation method and a related device. The method comprises: obtaining application operation data of a user; obtaining candidate recommendation information and high frequency behavior data sets corresponding thereto, wherein the number of appearances of at least any one piece of high frequency behavior data in the high frequency behavior data sets, together in history operation data of sample recommendation users is greater than a preset appearance threshold; respectively comparing high frequency behavior data under behavior tags in the high frequency behavior data sets with user behavior data under the same behavior tag in the application operation data; determining a target high frequency behavior data set in which the high frequency behavior data under each behavior tag matches the user behavior data under the same behavior tag in the application operation data; and recommending target recommendation information corresponding to the target high frequency behavior data set to the user in real time. The method can improve the flexibility and effectiveness of recommending information to a user.

Description

An information recommendation method and related equipment

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on March 30, 2020, the application number is 202010240351.5, and the application name is "an information recommendation method and related equipment", the entire content of which is incorporated into this application by reference middle.

Technical field

This application relates to the field of terminal technology, and in particular to an information recommendation method and related equipment.

Background technique

With the development of the Internet and terminal technology, various terminal APPs (Applications) emerge in an endless stream, satisfying the needs of users in all aspects of life and work. During the user's use of the APP, the APP operator usually generates some information recommended to the user, such as some activity participation information, some product information, and so on.

In the current information recommendation method, the APP operator usually generates unified recommendation information for all customers, and after the user triggers the recommendation interface in the APP, the generated unified recommendation information is recommended to the user. The inventor found that this information recommendation method for recommending uniform information to users without difference is relatively simple, so that after a large part of users receive the recommendation information, they are ignored as invalid information, and the effectiveness of the recommendation information is reduced.

Application content

This application provides an information recommendation method and related equipment. Through this application, the flexibility and effectiveness of recommending information to users in the process of using terminal applications can be improved.

The first aspect of the embodiments of the present application provides an information recommendation method, including:

Acquiring application operation data of the user for the first application, where the application operation data includes user behavior data under different behavior tags;

Obtain at least one candidate recommendation information of the first application, and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set contains high-frequency behavior data under different behavior tags, and the high-frequency behavior data The behavior data is obtained based on the historical operation data of the multiple sample recommendation users corresponding to the candidate recommendation information. The historical operation data includes sample behavior data under different behavior tags; the high-frequency behavior data set corresponding to the candidate recommendation information Any high-frequency behavior data in the candidate recommendation information, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset occurrence number threshold, and the high-frequency behavior data set corresponding to the candidate recommendation information includes In the case of multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information The number of times is greater than the preset threshold of occurrence times;

Comparing the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data;

A target high-frequency behavior data set is determined from the high-frequency behavior data set corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set is the same as the application operation data. User behavior data under the same behavior label in the matching;

The candidate recommendation information corresponding to the target high-frequency behavior data set is determined as target recommendation information, and the target recommendation information is recommended to the user in real time.

The second aspect of the embodiments of the present application provides an information recommendation device, including:

A data acquisition module, configured to acquire user application operation data for the first application, where the application operation data includes user behavior data under different behavior tags;

The collection acquisition module is configured to acquire at least one candidate recommendation information of the first application and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set includes high-frequency behaviors under different behavior tags Data, the high-frequency behavior data is obtained based on the historical operation data of multiple sample recommendation users corresponding to the candidate recommendation information, the historical operation data includes sample behavior data under different behavior tags; the candidate recommendation information corresponds to For any high-frequency behavior data in the high-frequency behavior data set of the candidate recommendation information, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold, and the number of occurrences in the candidate recommendation information corresponds to When the high-frequency behavior data set contains multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the sample recommendation corresponding to the candidate recommendation information The number of times in the user's historical operation data is greater than the preset number of occurrences threshold;

The comparison module is used to compare the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data. ；

The target set determination module is configured to determine a target high-frequency behavior data set from the high-frequency behavior data sets corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set, All match the user behavior data under the same behavior label in the application operation data;

The information recommendation module is configured to determine candidate recommendation information corresponding to the target high-frequency behavior data set as target recommendation information, and recommend the target recommendation information to the user in real time.

A third aspect of the embodiments of the present application provides an information recommendation device, including: a processor and a memory;

The processor is connected to a memory, where the memory is used to store program code, and the processor is used to call the program code to execute the method in any one of the foregoing aspects in the embodiments of the present application.

The fourth aspect of the embodiments of the present application provides a computer storage medium, the computer storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the method in any of the foregoing aspects.

The embodiment of the application can mine the historical operation data of the recommended users of each candidate recommendation information sample to obtain the high-frequency behavior data set corresponding to each candidate recommendation information, and then through the high-frequency behavior data set corresponding to each candidate recommendation information and the application operation data The comparison determines the user's personalized target recommendation information, realizes the differentiated recommendation information based on the user's application operation data, and improves the flexibility and effectiveness of the information recommended to the user.

Description of the drawings

FIG. 1 is an architecture diagram of an information recommendation system provided by an embodiment of the present application;

FIG. 2 is a schematic flowchart of an information recommendation method provided by an embodiment of the present application;

FIG. 3 is a schematic diagram of determining a frequent item set of behavior data according to an embodiment of the present application;

Fig. 4a is a schematic diagram of a waiting image display of a first recommended object provided by an embodiment of the present application;

FIG. 4b is a schematic diagram of another waiting image display of a first recommended object provided by an embodiment of the present application;

FIG. 5 is a schematic diagram of an interactive image display of a first recommended object provided by an embodiment of the present application;

FIG. 6 is a schematic flowchart of another information recommendation method provided by an embodiment of the present application;

FIG. 7 is a schematic structural diagram of an information recommendation device provided by an embodiment of the present application;

FIG. 8 is a schematic structural diagram of another information recommendation device provided by an embodiment of the present application.

Detailed ways

The information recommendation method and related equipment provided by the embodiments of the present application will be described below in conjunction with FIG. 1 to FIG. 8.

Referring to Figure 1, Figure 1 is an architecture diagram of an information recommendation system provided by an embodiment of the present application. As shown in the figure, the information recommendation system may at least include a server A and a terminal cluster, and the terminal cluster may include at least one terminal. , Figure 1 exemplarily shows three terminals: terminal b, terminal c, and terminal d. The terminals in the terminal cluster may include, but are not limited to, mobile phones, tablets, smart wearable devices, and other terminals; server A is for operating a certain The server of the application (APP), the terminal in the terminal cluster is installed with the APP, and the server A can recommend information to the user through the APP installed in the terminal. As shown in Fig. 1, a network connection can be established between the terminal b, the terminal c, and the terminal d and the server A, so that each terminal and the server A can exchange data through the network connection. The following describes the information recommendation method provided by the embodiment of the present application with reference to FIG. 2 and FIG. 6. The information recommendation method corresponding to FIG. 2 and FIG. 6 can be implemented based on the information recommendation system described in FIG.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of an information recommendation method provided by an embodiment of the present application. As shown in the figure, the method may include steps S201 to S205.

S201: Acquire application operation data of the user for the first application.

Here, it is possible to obtain the user’s application operation data for the first application in the specified first time period before the current time. For example, if the current time is 13 o’clock, the time half an hour before the current time can be used as the specified first time period. Time period, that is, to obtain the user's application operation data between 12:30 and 13:00.

Specifically, the user's operation record in the first time period may be obtained first, and then the user's application operation data in the first time period may be counted. The application operation data may include user behavior data under different behavior tags. Behavior tags are preset tags used to indicate different types of behavior data of the user in the first time period. For example, if the first application is an insurance introduction and sales application, the user’s operation record during the first preset time period includes browsing a cardiovascular treatment article between 12:3:5 and 12:05:05. Browse a cardiovascular health article between 12:5:50 and 12:07:50, and browse the article on cardiovascular health for the elderly between 12:8 and 12:13; preset Behavior tags can include the number of chronic disease articles, the average browsing time of chronic disease articles, the number of acute disease articles, the average browsing time of acute disease articles, the number of insurance introduction articles, the average browsing time of insurance introduction articles, then In the user's application operation data between 12:30 and 13:00, the user behavior data corresponding to the behavior tag of the number of articles browsing chronic diseases is 3, the average browsing time of articles about chronic diseases is 3 minutes, the number of articles browsing acute diseases, The user behavior data corresponding to the four behavior labels of the average browsing time of the number of acute disease articles, the number of browsing insurance introduction articles, and the average browsing time of insurance introduction articles are all 0.

S202. Obtain at least one candidate recommendation information of the first application and a high-frequency behavior data set corresponding to each candidate recommendation information.

Among them, the first application may have multiple candidate recommendation information, where the candidate recommendation information may be products, services, activities, etc. recommended to the user, and optionally may also include purchase or participation links corresponding to these products, services, and activities. Each candidate recommendation information has its own corresponding high-frequency behavior data set. The high-frequency behavior data set contains high-frequency behavior data under different behavior tags. The high-frequency behavior data is the historical operation of recommending users based on multiple samples corresponding to the candidate recommendation information Based on the data, the historical operation data contains sample behavior data under different behavior labels. Any one of the high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information has appeared in the historical operation data of the sample recommended user corresponding to the candidate recommendation information for a number of times greater than the preset threshold of occurrence times, and is in the candidate recommendation information corresponding to the high-frequency behavior data. When the high-frequency behavior data set contains multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the sample recommendation information corresponding to the candidate recommendation information. The historical operation of the recommended user The number of times in the data is greater than the preset number of occurrences threshold. For example, a certain high-frequency behavior data set {S, T}, then the number of occurrences of S in the corresponding historical operation data is greater than the preset number of occurrences threshold, and the number of occurrences of T in the corresponding historical operation data is greater than the preset number of occurrences Threshold, the number of times that S and T appear together in the same historical operation data is greater than the preset number of times of occurrence threshold.

Here, the determination of the high-frequency behavior data set corresponding to each candidate recommendation information can be determined by the Apriori algorithm. The Apriori algorithm is an association relationship mining algorithm through which the historical operation data of the user can be recommended from the sample of each candidate recommendation information Mining to get the high-frequency behavior data set. The sample recommended users of the candidate recommendation information may be the historical recommended users of the candidate recommendation information, or the sample users of the candidate recommendation information by the operator of the APP. The historical operation data of sample recommendation users also includes sample behavior data under different behavior labels.

First, the Apriori algorithm is used to determine the frequent item sets of behavior data of each candidate recommendation information, and then the high-frequency behavior data sets of each candidate recommendation information are determined according to the frequent itemsets of behavior data. Here, taking the frequent itemsets of one of the candidate recommendation information as an example, the Apriori algorithm is combined to introduce the process of determining the frequent itemsets of behavior data.

Firstly, preprocess the discrete value sample behavior data in the historical operation data corresponding to the candidate recommendation information, and preprocess it into continuous value segment type sample behavior data. For some numerical behavior labels, you can segment the possible values of the behavior label in advance to obtain different segmented continuous data under the behavior label, and then replace the data under the behavior label in the historical operation data with The segment continuous data to which it belongs. For example, for the behavior label of the number of articles on chronic diseases, three corresponding segmented continuous data can be pre-divided, which are 0-2, 3-6, and 6 or more. If a sample recommends that the number of articles on chronic diseases is 3 , It can be pre-processed to 3-6.

Furthermore, after preprocessing, each behavior label in each historical operation data corresponding to the candidate recommendation information corresponds to each possible sample behavior data to form multiple candidate item sets, and by combining the samples contained in each candidate item set The number of occurrences of behavior data in the preprocessed historical operation data is compared with the preset threshold of occurrences, and the number of occurrences of the sample behavior data included in the set of multiple candidates is not limited to the candidate with the preset threshold of occurrences. The itemsets are determined as frequent item sets;

If there is only one frequent item set, the frequent item set is determined as the frequent item set of behavior data corresponding to the candidate recommendation information; if the frequent item set contains more than one item, the item data contained in any frequent item set is combined with The sample behavior data in other frequent item sets are combined with the sample behavior data contained in other frequent item sets to form a candidate binomial set. The sample behavior data contained in each candidate binomial set appear together in the preprocessed historical operation data. The number of times, after comparing with the preset threshold of occurrences, the candidate binomial set with the included sample behavior data co-occurring not less than the preset threshold of occurrences is determined as a frequent binomial set; if there is no included sample behavior data If the number of co-occurrences is not less than the preset threshold of the number of occurrences of candidate binomial sets, multiple frequent item sets are determined as frequent item sets of behavior data;

If there is only one frequent binomial set, the frequent binomial set is determined as the frequent itemset of the behavior data; if the frequent binomial set contains more than one, the frequent binomial sets with only one sample with different behavior data The item sets are combined in pairs to form a three-item candidate set, and the number of times that the sample behavior data contained in the three-item candidate set co-occurs in the pre-processed historical operation data is compared with the preset threshold of the number of occurrences; By analogy, until it is determined that only one frequent L item set is included, the frequent L item set is determined as the behavior data frequent item set, or until the candidate M item set is determined, there is no history of the included sample behavior data after preprocessing In the operation data, the number of common occurrences of candidate M itemsets is not less than the preset occurrence number threshold, and multiple frequent M-1 itemsets are determined as behavioral data frequent itemsets. It can be understood that, in the above process, only one sample behavior data under the same behavior label in the multiple candidate sets generated by the combination is included.

Referring to Figure 3, Figure 3 is a schematic diagram of determining a frequent item set of behavioral data provided by an embodiment of the present application. It will be described with reference to Figure 3 as an example. First, refer to Table 1. If a certain candidate recommendation information corresponds to 10 sample recommended users, The historical behavior data under each behavior label in the preprocessed historical operands is shown in Table 1:

Table 1

Here, A is used to represent the behavior label of the number of articles browsing chronic diseases, and A1, A2, A3, and A4 are used to represent the four sample behavior data of 0-2, 3-6, 6-10, and 10 under label A, respectively. Use B to represent the behavior label of the average browsing time of chronic disease articles, use B1, B2, B3, and B4 to represent the three sample behavior data of 0-5, 6-10, and above 10 under label B, and use C to represent browsing insurance The behavior label of category introduction article number, use C1, C2, C3, and C4 to represent the four sample behavior data of 0-3, 4-8, 9-12, and above 12 under label C, and D to indicate whether to open insurance products For the behavior label of the home page, use D1 and D2 to represent the two sample behavior data of yes and no under label D. Then, after the determination process shown in Fig. 3, the corresponding frequent itemsets of behavior data are obtained as frequent three itemsets {B1, C1, D2}.

After the frequent item set of behavior data is determined, the frequent item set of behavior data corresponding to the candidate recommendation information is used to determine the high-frequency behavior data set corresponding to the candidate recommendation information. In an optional way, the frequent item set of behavior data can be directly determined as the high-frequency behavior data set. In another implementation manner, when there are multiple frequent item sets of behavior data sets corresponding to some candidate recommendation information, the confidence of each behavior data frequent item set corresponding to the candidate recommendation information can be determined, and then the confidence of each frequent item set corresponding to the candidate recommendation information can be determined. Confidence: Determine the high-frequency behavior data set from multiple frequent items of behavior data.

Specifically, for the first candidate recommendation information that contains multiple frequent item sets of behavior data in the candidate recommendation information, any one of its corresponding behavior labels can be designated as the confidence level for determining the corresponding behavior data frequent item sets. The behavioral data frequent item set of a sample behavior data, the corresponding confidence degree is based on the sample behavior data contained in the behavioral data frequent item set, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the first candidate recommendation information, and the first candidate recommendation information. The behavior data frequent items corresponding to the candidate recommendation information set the sample behavior data under the specified behavior label, and the ratio of the number of times that appear in the historical operation data of the sample recommendation user corresponding to the first candidate recommendation information is determined. For a frequent item set of behavior data that contains multiple sample behavior data, the corresponding confidence level appears in the historical operation data of the sample recommended user corresponding to the first candidate recommendation information through the sample behavior data included in the behavior data frequent item set. The number of times is determined by the ratio of the number of times that the sample behavior data under the specified behavior label in the behavior data frequent item set corresponding to the first candidate recommendation information appears in the historical operation data of the sample recommended user corresponding to the first candidate recommendation information. Furthermore, the behavior data frequent item set whose confidence is higher than the preset confidence threshold is determined as the high-frequency behavior data set corresponding to the first candidate recommendation information.

The high-frequency behavior data set of the candidate recommendation information can be determined through the foregoing process, and the determined high-frequency behavior data set can be obtained in step S202.

S203: Compare the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data.

Here, in the process of comparing the high-frequency behavior data set of any one of the candidate recommendation information, the user behavior of each behavior tag in the high-frequency behavior data set of the candidate recommendation information can be obtained from the user's application operation data Data, and then compare each high-frequency behavior data in the high-frequency behavior data set of the candidate recommendation information with the user behavior data under the same behavior tag in the application operation data.

In an optional implementation, the comparison of high-frequency behavior data with user behavior data under the same behavior tag can compare whether the high-frequency behavior data is equal to the user behavior data under the same behavior tag, or whether it contains the same behavior If the user behavior data under the label meets one of the two, it can be considered that the two match. For example, if the high-frequency behavior data under the behavior label of whether to open the insurance interface is "Yes", and the user behavior data under the same behavior label is "Yes", the two match. For another example, if the high-frequency behavior data under the behavior tag of browsing time is 3min-10min, and the user behavior data under the same behavior tag is 5min, then the two match.

In another optional implementation manner, the similarity between high-frequency behavior data and user behavior data under the same behavior tag can be judged, and if the similarity is higher than a preset similarity threshold, the two can be considered to match. The method for determining the similarity can be preset for high-frequency behavior data of the numerical value segment. The ratio of the intermediate value to the value obtained after taking the absolute value is determined as the similarity; another example is for user behavior data that is less than the left end point value in the high-frequency behavior data value segment, the user behavior data can be compared with the left value in the value segment. The side endpoint value is subtracted from the user behavior data under the same behavior label, and the ratio of the left endpoint value in the value segment to the above subtracted difference is determined as the similarity; for high-frequency behavior data The user behavior data of the right endpoint value in the value segment can be subtracted from the user behavior data under the same behavior label with the value of the right endpoint in the value segment, and the value of the right endpoint in the value segment is the same as the above The ratio of the difference after subtraction is determined as the similarity. For example, if the high-frequency behavior data corresponding to the behavior tag of browsing time is 3min-7min, and the user behavior data under the same behavior tag is 9min, if the similarity determination method in the first example above is followed, the similarity between the two is Is 1.25. According to the similarity determination method in the second example above, the similarity between the two is 0.286.

S204: Determine a target high-frequency behavior data set from the high-frequency behavior data set corresponding to each candidate recommendation information.

Here, according to the result of the comparison in step S203, from the high-frequency behavior data set corresponding to each candidate recommendation information, it is determined that the high-frequency behavior data under each behavior tag is the same as the user behavior under the same behavior tag in the application operation data. The target high-frequency behavior data collection for data matching.

S205: Determine candidate recommendation information corresponding to the target high-frequency behavior data set as target recommendation information, and recommend the target recommendation information to the user in real time.

Here, the method of recommending the target recommendation information to the user in real time may be a method of broadcasting candidate recommendation information through real-time voice, recommending the target recommendation information in real time, or recommending the target recommendation information in the form of text in real time.

In an alternative implementation manner, the target recommendation information includes a designated jump link, and the first recommendation object can be set in the first application. The first recommendation object is a virtual object that shows the recommendation information to the user. Before S205, the target recommendation When the information has not been determined, the first recommended object may be in a waiting state, and accordingly, the waiting image of the first recommended object may be displayed in the first application. Referring to Figure 4a, Figure 4a is a schematic diagram of a waiting image display of a first recommended object provided by an embodiment of the present application. As shown in Figure 4a, before the target recommendation information is determined, the waiting image of the first recommended object may be transparent Or a semi-transparent image, displayed in the interface of the first application in real time. Referring to Figure 4b, Figure 4b is a schematic diagram showing another waiting image of the first recommended object provided by an embodiment of the present application. After the first application is started, the first recommended object can dynamically switch through the waiting image in the left image in Figure 4b. To the waiting image on the right in Figure 4b.

After the target recommendation information is determined, an interactive image in which the first recommendation object recommends the target recommendation information can be generated according to the information text of the target recommendation information, and the generated interactive image is displayed. Refer to FIG. 5. FIG. 5 is a schematic diagram of an interactive image display of a first recommended object provided by an embodiment of the present application. If the target recommendation information is information for recommending X services, the generated interactive image may be as shown in FIG. 5. Dynamic interactive images can improve the user's response to the target recommendation information, and further improve the effectiveness of the target recommendation information.

In this embodiment of the application, the user’s application operation data for the first application is acquired, and at least one candidate recommendation information of the first application and the respective corresponding high-frequency behavior data sets are acquired, and the high-frequency behavior data corresponding to each candidate recommendation information is obtained The high-frequency behavior data under each behavior tag in the set is compared with the user behavior data under the same behavior tag in the application operation data, so as to determine each behavior tag from the high-frequency behavior data set corresponding to each candidate recommendation information The high-frequency behavior data under the application operation data is the target high-frequency behavior data set that matches the user behavior data under the same behavior label in the application operation data, and then the candidate recommendation information corresponding to the target high-frequency behavior data set is determined as the target recommendation information, And recommend target recommendation information to users in real time.

Among them, the high-frequency behavior data is obtained based on the historical operation data of multiple sample recommendation users corresponding to the candidate recommendation information. Any high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appears in the candidate recommendation information correspondence. The sample recommends that the number of times in the user’s historical operation data is greater than the preset number of occurrences threshold, and when the high-frequency behavior data set corresponding to the candidate recommendation information contains multiple high-frequency behavior data, the high-frequency behavior data corresponding to the candidate recommendation information The number of times that any plurality of high-frequency behavior data in the set appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset occurrence number threshold. Therefore, by mining each candidate recommendation information sample to recommend the user's historical operation data, the high-frequency behavior data set corresponding to each candidate recommendation information is obtained, and then the high-frequency behavior data set corresponding to each candidate recommendation information is compared with the application operation data. , Determine the user's personalized target recommendation information, realize the differentiated recommendation information based on the user's application operation data, and improve the flexibility and effectiveness of the information recommended to the user.

Referring to FIG. 6, FIG. 6 is a schematic flowchart of another information recommendation method provided by an embodiment of the present application. As shown in the figure, the method may include the following steps:

S601: Obtain application operation data of a user for a first application, and obtain user attribute data of the user under different attribute tags.

The application operation data includes user behavior data under different behavior tags

S602. Acquire at least one candidate recommendation information of the first application and a high-frequency behavior data set corresponding to each candidate recommendation information.

The high-frequency behavior data set includes high-frequency behavior data under different behavior labels and high-frequency attribute data under different attribute labels. The high-frequency behavior data and the high-frequency attribute data are obtained based on the historical operation data of a plurality of sample recommended users corresponding to the candidate recommendation information and the sample user data of the sample recommended users, and the historical operation data includes different behavior tags The sample behavior data under the sample user data includes sample attribute data under different attribute tags.

Wherein, the number of occurrences of any high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than a preset occurrence number threshold. The number of times that any high-frequency attribute data in the high-frequency behavior data set corresponding to the candidate recommendation information appears in the sample user data of the sample recommended user corresponding to the candidate recommendation information is greater than a preset occurrence number threshold. In the case that the high-frequency behavior data set corresponding to the candidate recommendation information contains multiple high-frequency behavior data, any plurality of high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in all the high-frequency behavior data sets. The number of times in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold. In the case where the high-frequency behavior data set corresponding to the candidate recommendation information contains multiple high-frequency attribute data, any plurality of high-frequency attribute data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in all the high-frequency behavior data sets. The number of times in the sample user data of the sample recommended users corresponding to the candidate recommendation information is greater than the preset occurrence number threshold. In addition, in the high-frequency behavior data set corresponding to the candidate recommendation information, any high-frequency behavior data and any high-frequency attribute data, among the sample recommended users corresponding to the candidate recommendation information, the same sample recommends users The corresponding number of occurrences in the historical operation data and sample user data is greater than the preset threshold of occurrences.

For the mining of the high-frequency behavior data set, reference may be made to the specific implementation manner of embodiment S202 in FIG. 2, and details are not repeated here.

S603: Compare the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data.

S604: Compare the high-frequency attribute data under each attribute tag in each of the high-frequency behavior data sets with the user attribute data under the same attribute tag.

Wherein, step S604 may be executed after step S603, may also be executed before S603, or may be executed simultaneously with S603. For the specific comparison manner, please refer to the implementation manner in S203 in the corresponding embodiment of FIG. 2, which is not repeated here.

S605: Match high-frequency behavior data under each behavior label with user behavior data under the same behavior label, and high-frequency behavior data in which the high-frequency attribute data under each attribute label matches the user attribute data under the same attribute label The collection is determined as the target high-frequency behavior data collection.

S606: Determine candidate recommendation information corresponding to the target high-frequency behavior data set as target recommendation information.

S607: According to the user attribute data of the user under the designated attribute tag, determine the recommendation mode information of the user under the column of different recommendation modes.

Among them, the recommendation method column may include one or more of a recommended font column, a recommended target image column, a recommended voice column, or a recommended style column. There are different column options for different recommendation methods. For example, the recommended font column can have brush writing, swash font, Martian font, bold font, etc., and the recommended object image column can have pet images, two-dimensional cartoon images, and ancient styles. Image and other options, the recommended voice column can have options such as sweet beautiful voice, magnetic male voice, funny voice, etc., and the recommended style column can have options such as classical Chinese style, vernacular style, and dialect style. Pre-set designated attribute tags for the different recommendation method columns, and then determine the recommendation method information under the different recommendation method columns according to the user attribute data of the user under the designated attribute label. For example, the specified attribute tags are gender, age, and occupation. If the user's gender is male, age is 20-40, and the occupation is an engineer, he can recommend the font column, the recommended object image column, the recommended voice column or The recommended method information under the recommended style of writing column is determined to be brush writing, ancient style image, sweet and beautiful voice, and vernacular style. The user’s gender is female, age is 14 and his occupation is a student. The recommendation method information under the recommended font column, recommended object image column, recommended voice column or recommended style column can be determined as Martian text, two-dimensional cartoon image, Funny voice, vernacular style.

S608: Determine a target recommendation method of the user according to the recommendation method information, and recommend the target recommendation information in real time according to the target recommendation method.

If the recommended method column in S607 includes a recommended voice column, the corresponding voice is used to broadcast the target recommendation information. If the recommended method column in S607 includes a recommended font column, a recommended object image column, or a recommended style column, then the target text recommendation information information The text is nested, and the text frame is preset for the recommended method information under the recommended style column, and then the nested text is based on the font of the recommended method information under the recommended font column, and the recommended image corresponding to the recommended method information under the recommended object image column , Composite display images, and display the composite images in the interface of the first application in real time.

In the embodiment of this application, by mining the historical operation data and sample user data of the recommended users of each candidate recommendation information sample, the high-frequency behavior data set corresponding to each candidate recommendation information is obtained, and then the high-frequency behavior data corresponding to each candidate recommendation information is obtained. Collect, compare with the user's application operation data and user attribute data, determine the user's personalized target recommendation information, determine the corresponding target recommendation method according to the user's attribute information, and recommend the target recommendation information to the user according to the target recommendation method. It realizes the differentiated generation and recommendation information according to the user's application operation data, and improves the flexibility and effectiveness of recommending information to the user.

Referring to FIG. 7, FIG. 7 is a schematic structural diagram of an information recommendation apparatus provided by an embodiment of the present application. As shown in the figure, the information recommendation apparatus 70 includes:

The data acquisition module 701 is configured to acquire user application operation data for the first application, where the application operation data includes user behavior data under different behavior tags;

The collection acquisition module 702 is configured to acquire at least one candidate recommendation information of the first application and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set includes high-frequency behavior data under different behavior tags. Behavioral data, where the high-frequency behavioral data is obtained based on the historical operation data of a plurality of sample recommendation users corresponding to the candidate recommendation information, and the historical operation data includes sample behavior data under different behavior labels; the candidate recommendation information For any high-frequency behavior data in the corresponding high-frequency behavior data set, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold, and the number of occurrences in the candidate recommendation information corresponds to In the case where the high-frequency behavior data set contains multiple high-frequency behavior data, any plurality of high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the sample corresponding to the candidate recommendation information It is recommended that the number of times in the historical operation data of the user is greater than the preset number of occurrences threshold;

The comparison module 703 is configured to compare the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data;

The target set determining module 704 is configured to determine a target high-frequency behavior data set from the high-frequency behavior data sets corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set , All match the user behavior data under the same behavior label in the application operation data;

The information recommendation module 705 is configured to determine candidate recommendation information corresponding to the target high-frequency behavior data set as target recommendation information, and recommend the target recommendation information to the user in real time.

In specific implementation, the information recommendation device 70 can execute each step in the information recommendation method shown in FIGS. 2 and 6 through its built-in functional modules. For specific implementation details, please refer to the respective embodiments in FIGS. 2 and 6 The implementation details of the steps will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated.

Referring to FIG. 8, FIG. 8 is a schematic structural diagram of another information recommendation apparatus provided by an embodiment of the present application. As shown in FIG. 8, the information recommendation device 80 may include: at least one processor 801, such as a CPU, at least one network interface 804, a user interface 803, a memory 805, and at least one communication bus 802. Among them, the communication bus 802 is used to implement connection and communication between these components. The user interface 803 may include a display screen (Display) and a keyboard (Keyboard), and the optional user interface 803 may also include a standard wired interface and a wireless interface. The network interface 804 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 805 may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory 805 may also be at least one storage device located far away from the aforementioned processor 801. As shown in FIG. 8, the memory 805 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a device control application program.

In the information recommendation device 80 shown in FIG. 8, the network interface 804 is mainly used to connect to the terminal where the first application is installed; and the user interface 803 is mainly used to provide an input interface for the user; and the processor 801 can be used to call the memory 805 The device control application stored in to achieve:

Acquiring application operation data of the user for the first application, where the application operation data includes user behavior data under different behavior tags;

Obtain at least one candidate recommendation information of the first application, and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set contains high-frequency behavior data under different behavior tags, and the high-frequency behavior data The behavior data is obtained based on the historical operation data of the multiple sample recommendation users corresponding to the candidate recommendation information. The historical operation data includes sample behavior data under different behavior tags; the high-frequency behavior data set corresponding to the candidate recommendation information Any high-frequency behavior data in the candidate recommendation information, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset occurrence number threshold, and the high-frequency behavior data set corresponding to the candidate recommendation information includes In the case of multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information The number of times is greater than the preset threshold of occurrence times;

Comparing the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data;

A target high-frequency behavior data set is determined from the high-frequency behavior data set corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set is the same as the application operation data. User behavior data under the same behavior label in the matching;

The candidate recommendation information corresponding to the target high-frequency behavior data set is determined as target recommendation information, and the target recommendation information is recommended to the user in real time.

In an optional manner, the high-frequency behavior data set further includes high-frequency attribute data under different attribute tags;

The processor 801 is further configured to call the program code to execute:

Obtaining user attribute data of the user under different attribute tags;

Comparing the high-frequency attribute data under each attribute tag in each of the high-frequency behavior data sets with the user attribute data under the same attribute tag;

The high-frequency behavior data set through which the high-frequency behavior data under each behavior label is compared and determined as the target high-frequency behavior data set includes:

A collection of high-frequency behavior data that matches the high-frequency behavior data under each behavior label with the user behavior data under the same behavior label, and the high-frequency attribute data under each attribute label matches the user attribute data under the same attribute label, Determined as the target high-frequency behavior data collection.

In an optional manner, according to the user attribute data of the user under the designated attribute tag, the recommendation method information of the user under different recommendation method columns is determined, and the recommendation method column includes a recommended font column and a recommended target image One or more of the column, the recommended voice column or the recommended style column;

According to the recommendation method information, a target recommendation method of the user is determined, and the target recommendation method is recommended by the user in real time by the target recommendation information.

In an optional manner, the target recommendation information includes a designated jump link; before the target recommendation information is recommended to the user in real time, the display interface of the first application includes the first recommendation object The waiting image; the first recommended object is a virtual object that shows recommended information to the user; the waiting image is a display image corresponding to the first recommended object when the target recommendation information is not determined;

The processor 801 is configured to call the program code to specifically execute:

Generating an interactive image in which the first recommendation object recommends the target recommendation information according to the information text of the target recommendation information, and displaying the interactive image;

In the case of receiving the user's confirmation interaction instruction for the interaction image, the instruction jump link in the target recommendation information is opened.

In an optional manner, obtaining historical operation data of multiple sample recommended users corresponding to each of the candidate recommendation information;

Preprocessing the discrete numerical type sample behavior data in the historical operation data into continuous numerical segment type sample behavior data;

Acquiring the sample behavior data under any at least one behavior label from the historical operation data of the sample recommended user corresponding to the candidate recommendation information after preprocessing, to form a candidate frequent item set corresponding to the candidate recommendation information;

From the candidate frequent item set corresponding to the candidate recommendation information, determine the behavior data frequent item set corresponding to the candidate recommendation information; any sample behavior data in the behavior data frequent item set appears in the candidate recommendation information corresponding to the The number of times in the historical operation data of the sample recommended user is greater than the preset number of occurrence thresholds, and when the candidate frequent item set corresponding to the candidate recommendation information contains multiple sample behavior data, any number of frequent items in the behavior data set The number of times that the sample behavior data appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold;

According to the frequent item sets of behavior data corresponding to each candidate recommendation information, the high-frequency behavior data set corresponding to each candidate recommendation information is determined.

It should be understood that the information recommendation device 80 described in the embodiment of the present application can perform the description of the information recommendation method in the foregoing embodiment corresponding to FIG. 2 or FIG. 6, and may also perform the foregoing description of the information recommendation method in the foregoing embodiment corresponding to FIG. The description of the information recommendation device 1 will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated.

In addition, it needs to be pointed out here that: the embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium may be non-volatile or volatile, and the computer-readable storage medium may be The storage medium stores the computer program executed by the information recommendation device 70 mentioned above, and the computer program includes program instructions. When the processor executes the program instructions, it can execute:

Acquiring application operation data of the user for the first application, where the application operation data includes user behavior data under different behavior tags;

Obtain at least one candidate recommendation information of the first application, and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set contains high-frequency behavior data under different behavior tags, and the high-frequency behavior data The behavior data is obtained based on the historical operation data of the multiple sample recommendation users corresponding to the candidate recommendation information. The historical operation data includes sample behavior data under different behavior tags; the high-frequency behavior data set corresponding to the candidate recommendation information Any high-frequency behavior data in the candidate recommendation information, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset occurrence number threshold, and the high-frequency behavior data set corresponding to the candidate recommendation information includes In the case of multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information The number of times is greater than the preset threshold of occurrence times;

Comparing the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data;

A target high-frequency behavior data set is determined from the high-frequency behavior data set corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set is the same as the application operation data. User behavior data under the same behavior label in the matching;

The candidate recommendation information corresponding to the target high-frequency behavior data set is determined as target recommendation information, and the target recommendation information is recommended to the user in real time.

In an optional manner, the high-frequency behavior data set further includes high-frequency attribute data under different attribute tags;

When the program instructions are executed by the processor, they also cause the processor to execute:

Obtaining user attribute data of the user under different attribute tags;

Comparing the high-frequency attribute data under each attribute tag in each of the high-frequency behavior data sets with the user attribute data under the same attribute tag;

The high-frequency behavior data set through which the high-frequency behavior data under each behavior label is compared and determined as the target high-frequency behavior data set includes:

A collection of high-frequency behavior data that matches the high-frequency behavior data under each behavior label with the user behavior data under the same behavior label, and the high-frequency attribute data under each attribute label matches the user attribute data under the same attribute label, Determined as the target high-frequency behavior data collection.

In an optional manner, when the program instructions are executed by the processor, they also cause the processor to execute:

According to the user attribute data of the user under the designated attribute tag, determine the user's recommendation method information under different recommendation method columns. The recommendation method column includes a recommended font column, a recommended target image column, a recommended voice column or a recommended style of writing One or more of the columns;

According to the recommendation method information, a target recommendation method of the user is determined, and the target recommendation method is recommended by the user in real time by the target recommendation information.

In an optional manner, the target recommendation information includes a designated jump link; before the target recommendation information is recommended to the user in real time, the display interface of the first application includes the first recommendation object The waiting image; the first recommended object is a virtual object that displays recommended information to the user; the waiting image is a display image corresponding to the first recommended object when the target recommendation information is not determined;

When the program instructions are executed by the processor, the processor specifically executes:

Generating an interactive image in which the first recommendation object recommends the target recommendation information according to the information text of the target recommendation information, and displaying the interactive image;

In the case of receiving the user's confirmation interaction instruction for the interaction image, the instruction jump link in the target recommendation information is opened.

In an optional manner, when the program instructions are executed by the processor, they also cause the processor to execute:

Acquiring historical operation data of multiple sample recommended users corresponding to each of the candidate recommendation information;

Preprocessing the discrete numerical type sample behavior data in the historical operation data into continuous numerical segment type sample behavior data;

Acquiring the sample behavior data under any at least one behavior label from the historical operation data of the sample recommended user corresponding to the candidate recommendation information after preprocessing, to form a candidate frequent item set corresponding to the candidate recommendation information;

From the candidate frequent item set corresponding to the candidate recommendation information, determine the behavior data frequent item set corresponding to the candidate recommendation information; any sample behavior data in the behavior data frequent item set appears in the candidate recommendation information The number of times in the historical operation data of the sample recommended user is greater than the preset number of occurrence thresholds, and when the candidate frequent item set corresponding to the candidate recommendation information contains multiple sample behavior data, any number of frequent items in the behavior data set The number of times that the sample behavior data appear together in the historical operation data of the sample recommended users corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold;

According to the frequent item sets of behavior data corresponding to each candidate recommendation information, the high-frequency behavior data set corresponding to each candidate recommendation information is determined.

When the processor executes the program instructions, it can execute the description of the information recommendation method in the foregoing embodiment corresponding to FIG. 2 or FIG. 6, therefore, it will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated. For technical details that are not disclosed in the embodiment of the computer-readable storage medium involved in this application, please refer to the description of the method embodiment of this application.

A person of ordinary skill in the art can understand that all or part of the processes in the methods of the above-mentioned embodiments can be implemented by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, it may include the procedures of the above-mentioned method embodiments. Wherein, the computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM), etc.

The above-disclosed are only preferred embodiments of this application, and of course the scope of rights of this application cannot be limited by this. Therefore, equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims (20)

1. An information recommendation method, wherein the method includes:

   Acquiring application operation data of the user for the first application, where the application operation data includes user behavior data under different behavior tags;

   Obtain at least one candidate recommendation information of the first application, and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set contains high-frequency behavior data under different behavior tags, and the high-frequency behavior data The behavior data is obtained based on the historical operation data of the multiple sample recommendation users corresponding to the candidate recommendation information. The historical operation data includes sample behavior data under different behavior tags; the high-frequency behavior data set corresponding to the candidate recommendation information Any high-frequency behavior data in the candidate recommendation information, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset occurrence number threshold, and the high-frequency behavior data set corresponding to the candidate recommendation information includes In the case of multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information The number of times is greater than the preset threshold of occurrence times;

   Comparing the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data;

   A target high-frequency behavior data set is determined from the high-frequency behavior data set corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set is the same as the application operation data. User behavior data under the same behavior label in the matching;

   The candidate recommendation information corresponding to the target high-frequency behavior data set is determined as target recommendation information, and the target recommendation information is recommended to the user in real time.
2. The method according to claim 1, wherein the high-frequency behavior data set further contains high-frequency attribute data under different attribute tags;

   The method also includes:

   Obtaining user attribute data of the user under different attribute tags;

   Comparing the high-frequency attribute data under each attribute tag in each of the high-frequency behavior data sets with the user attribute data under the same attribute tag;

   The high-frequency behavior data set through which the high-frequency behavior data under each behavior label is compared and determined as the target high-frequency behavior data set includes:

   A collection of high-frequency behavior data that matches the high-frequency behavior data under each behavior label with the user behavior data under the same behavior label, and the high-frequency attribute data under each attribute label matches the user attribute data under the same attribute label, Determined as the target high-frequency behavior data collection.
3. The method according to claim 2, wherein the high-frequency behavior data in the target high-frequency behavior data set is equal to user behavior data under the same behavior tag, or includes user behavior data under the same behavior tag.
4. The method according to claim 2 or 3, wherein the method further comprises:

   According to the user attribute data of the user under the designated attribute tag, determine the user's recommendation method information under different recommendation method columns. The recommendation method column includes a recommended font column, a recommended target image column, a recommended voice column or a recommended style of writing One or more of the columns;

   According to the recommendation method information, a target recommendation method of the user is determined, and the target recommendation method is recommended by the user in real time by the target recommendation information.
5. The method according to claim 1, wherein the target recommendation information includes a designated jump link; before the real-time recommendation of the target recommendation information to the user, the display interface of the first application includes the first A waiting image of a recommended object; the first recommended object is a virtual object that shows recommended information to the user; the waiting image is a display corresponding to the first recommended object when the target recommendation information is not determined image;

   The real-time recommendation of the target recommendation information to the user includes:

   Generating an interactive image in which the first recommendation object recommends the target recommendation information according to the information text of the target recommendation information, and displaying the interactive image;

   In the case of receiving the user's confirmation interaction instruction for the interaction image, the instruction jump link in the target recommendation information is opened.
6. The method according to claim 5, wherein said generating an interactive image in which said first recommendation object recommends said target recommendation information according to the information text of said target recommendation information comprises:

   In the case where the recommended column method includes a recommended font column, a recommended object image column or a recommended style column, the information text of the target text recommendation information is nested with a text frame preset for the recommended information under the recommended style column ；

   The nested text is combined with the recommended image corresponding to the recommended method information under the recommended object image column according to the font corresponding to the recommended method letter under the recommended font column to synthesize the interactive image shown.
7. The method according to claim 1, wherein the method further comprises:

   Acquiring historical operation data of multiple sample recommended users corresponding to each of the candidate recommendation information;

   Preprocessing the discrete numerical type sample behavior data in the historical operation data into continuous numerical segment type sample behavior data;

   Acquiring the sample behavior data under any at least one behavior label from the historical operation data of the sample recommended user corresponding to the candidate recommendation information after preprocessing, to form a candidate frequent item set corresponding to the candidate recommendation information;

   From the candidate frequent item set corresponding to the candidate recommendation information, determine the behavior data frequent item set corresponding to the candidate recommendation information; any sample behavior data in the behavior data frequent item set appears in the candidate recommendation information corresponding to the The number of times in the historical operation data of the sample recommended user is greater than the preset number of occurrence thresholds, and when the candidate frequent item set corresponding to the candidate recommendation information contains multiple sample behavior data, any number of frequent items in the behavior data set The number of times that the sample behavior data appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold;

   According to the frequent item sets of behavior data corresponding to each candidate recommendation information, the high-frequency behavior data set corresponding to each candidate recommendation information is determined.
8. The method according to claim 7, wherein the determining the high-frequency behavior data set corresponding to each of the candidate recommendation information according to the frequent item sets of behavior data corresponding to each of the candidate recommendation information comprises:

   When the behavior data frequent item sets corresponding to the first candidate recommendation information includes multiple, the confidence of each behavior data frequent item set corresponding to the first candidate recommendation information is determined; the first candidate recommendation information is any candidate recommendation information;

   In the case that the behavior data frequent item set corresponding to the first candidate recommendation information contains only one sample behavior data, the confidence level is the sample behavior data included in the behavior data frequent item set, which appears in the first candidate The number of times in the historical operation data of the sample recommended user corresponding to the recommendation information, the behavior data corresponding to the first candidate recommendation information, and the sample behavior data under the specified behavior label in the frequent item set. The historical operation of the sample recommended user corresponding to the first candidate recommendation information The ratio of the number of occurrences in the data;

   In the case that the behavior data frequent item set corresponding to the first candidate recommendation information includes multiple sample behavior data, the confidence is that each sample behavior data included in the behavior data frequent item set appears together in the first candidate recommendation information. The number of times in the historical operation data of the sample recommended user corresponding to the candidate recommendation information, the sample behavior data under the specified behavior label in the frequent item set of behavior data corresponding to the first candidate recommendation information The ratio of the number of occurrences in historical operation data;

   A frequent item set of behavior data with a confidence level higher than a preset confidence threshold is determined as a high-frequency behavior data set corresponding to the first candidate recommendation information.
9. The method according to claim 1, wherein the determining a target high-frequency behavior data set from the high-frequency behavior data sets corresponding to each of the candidate recommendation information comprises:

   Determine whether the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information is similar to the user behavior data under the same behavior tag in the application operation data is higher than the preset similarity Threshold

   The high-frequency behavior data under each behavior label is similar to the user behavior data under the same behavior label in the application operation data, and the high-frequency behavior data set is higher than the preset similarity threshold, and the target high frequency is determined Behavioral data collection.
10. An information recommendation device, which includes:

    A data acquisition module, configured to acquire user application operation data for the first application, where the application operation data includes user behavior data under different behavior tags;

    The collection acquisition module is configured to acquire at least one candidate recommendation information of the first application and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set includes high-frequency behaviors under different behavior tags Data, the high-frequency behavior data is obtained based on the historical operation data of multiple sample recommendation users corresponding to the candidate recommendation information, the historical operation data includes sample behavior data under different behavior labels; the candidate recommendation information corresponds to For any high-frequency behavior data in the high-frequency behavior data set of the candidate recommendation information, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold, and the number of occurrences in the candidate recommendation information corresponds to When the high-frequency behavior data set contains multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the sample recommendation corresponding to the candidate recommendation information The number of times in the user's historical operation data is greater than the preset number of occurrences threshold;

    The comparison module is used to compare the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data. ；

    The target set determination module is configured to determine a target high-frequency behavior data set from the high-frequency behavior data sets corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set, All match the user behavior data under the same behavior label in the application operation data;

    The information recommendation module is configured to determine candidate recommendation information corresponding to the target high-frequency behavior data set as target recommendation information, and recommend the target recommendation information to the user in real time.
11. An information recommendation device, which includes: a processor and a memory;

    The processor is connected to a memory, where the memory is used to store program code, and the processor is used to call the program code to execute:

    Acquiring application operation data of the user for the first application, where the application operation data includes user behavior data under different behavior tags;

    Obtain at least one candidate recommendation information of the first application, and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set contains high-frequency behavior data under different behavior tags, and the high-frequency behavior data The behavior data is obtained based on the historical operation data of the multiple sample recommendation users corresponding to the candidate recommendation information. The historical operation data includes sample behavior data under different behavior tags; the high-frequency behavior data set corresponding to the candidate recommendation information Any high-frequency behavior data in the candidate recommendation information, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset occurrence number threshold, and the high-frequency behavior data set corresponding to the candidate recommendation information includes In the case of multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information The number of times is greater than the preset threshold of occurrence times;

    Comparing the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data;

    A target high-frequency behavior data set is determined from the high-frequency behavior data set corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set is the same as the application operation data. User behavior data under the same behavior label in the matching;

    The candidate recommendation information corresponding to the target high-frequency behavior data set is determined as target recommendation information, and the target recommendation information is recommended to the user in real time.
12. The device according to claim 11, wherein the high-frequency behavior data set further includes high-frequency attribute data under different attribute tags;

    The processor is also used to call the program code to execute:

    Obtaining user attribute data of the user under different attribute tags;

    Comparing the high-frequency attribute data under each attribute tag in each of the high-frequency behavior data sets with the user attribute data under the same attribute tag;

    The high-frequency behavior data set through which the high-frequency behavior data under each behavior label is compared and determined as the target high-frequency behavior data set includes:

    A collection of high-frequency behavior data that matches the high-frequency behavior data under each behavior label with the user behavior data under the same behavior label, and the high-frequency attribute data under each attribute label matches the user attribute data under the same attribute label, Determined as the target high-frequency behavior data collection.
13. The apparatus according to claim 12, wherein the processor is further configured to call the program code to execute:

    According to the user attribute data of the user under the designated attribute tag, determine the user's recommendation method information under different recommendation method columns. The recommendation method column includes a recommended font column, a recommended target image column, a recommended voice column or a recommended style of writing One or more of the columns;

    According to the recommendation method information, a target recommendation method of the user is determined, and the target recommendation method is recommended by the user in real time by the target recommendation information.
14. The device according to claim 11, wherein the target recommendation information includes a designated jump link; before the real-time recommendation of the target recommendation information to the user, the display interface of the first application includes the first A waiting image of a recommended object; the first recommended object is a virtual object that shows recommended information to the user; the waiting image is a display corresponding to the first recommended object when the target recommendation information is not determined image;

    The processor is used to call the program code for specific execution:

    Generating an interactive image in which the first recommendation object recommends the target recommendation information according to the information text of the target recommendation information, and displaying the interactive image;

    In the case of receiving the user's confirmation interaction instruction for the interaction image, the instruction jump link in the target recommendation information is opened.
15. The apparatus according to claim 11, wherein the processor is further configured to call the program code to execute:

    Acquiring historical operation data of multiple sample recommended users corresponding to each of the candidate recommendation information;

    Preprocessing the discrete numerical type sample behavior data in the historical operation data into continuous numerical segment type sample behavior data;

    Acquiring the sample behavior data under any at least one behavior label from the historical operation data of the sample recommended user corresponding to the candidate recommendation information after preprocessing, to form a candidate frequent item set corresponding to the candidate recommendation information;

    From the candidate frequent item set corresponding to the candidate recommendation information, determine the behavior data frequent item set corresponding to the candidate recommendation information; any sample behavior data in the behavior data frequent item set appears in the candidate recommendation information corresponding to the The number of times in the historical operation data of the sample recommended user is greater than the preset number of occurrence thresholds, and when the candidate frequent item set corresponding to the candidate recommendation information contains multiple sample behavior data, any number of frequent items in the behavior data set The number of times that the sample behavior data appear together in the historical operation data of the sample recommended users corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold;

    According to the frequent item sets of behavior data corresponding to each candidate recommendation information, the high-frequency behavior data set corresponding to each candidate recommendation information is determined.
16. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the program instructions are executed by a processor, the processor executes:

    Acquiring application operation data of the user for the first application, where the application operation data includes user behavior data under different behavior tags;

    Obtain at least one candidate recommendation information of the first application, and a high-frequency behavior data set corresponding to each candidate recommendation information; the high-frequency behavior data set contains high-frequency behavior data under different behavior tags, and the high-frequency behavior data The behavior data is obtained based on the historical operation data of the multiple sample recommendation users corresponding to the candidate recommendation information. The historical operation data includes sample behavior data under different behavior tags; the high-frequency behavior data set corresponding to the candidate recommendation information Any high-frequency behavior data in the candidate recommendation information, the number of times that it appears in the historical operation data of the sample recommended user corresponding to the candidate recommendation information is greater than the preset occurrence number threshold, and the high-frequency behavior data set corresponding to the candidate recommendation information includes In the case of multiple high-frequency behavior data, any multiple high-frequency behavior data in the high-frequency behavior data set corresponding to the candidate recommendation information appear together in the historical operation data of the sample recommended user corresponding to the candidate recommendation information The number of times is greater than the preset threshold of occurrence times;

    Comparing the high-frequency behavior data under each behavior tag in the high-frequency behavior data set corresponding to each candidate recommendation information with the user behavior data under the same behavior tag in the application operation data;

    A target high-frequency behavior data set is determined from the high-frequency behavior data set corresponding to each candidate recommendation information, and the high-frequency behavior data under each behavior tag in the target high-frequency behavior data set is the same as the application operation data. User behavior data under the same behavior label in the matching;

    The candidate recommendation information corresponding to the target high-frequency behavior data set is determined as target recommendation information, and the target recommendation information is recommended to the user in real time.
17. The computer-readable storage medium according to claim 16, wherein the high-frequency behavior data set further contains high-frequency attribute data under different attribute tags;

    When the program instructions are executed by the processor, they also cause the processor to execute:

    Obtaining user attribute data of the user under different attribute tags;

    Comparing the high-frequency attribute data under each attribute tag in each of the high-frequency behavior data sets with the user attribute data under the same attribute tag;

    The high-frequency behavior data set through which the high-frequency behavior data under each behavior label is compared and determined as the target high-frequency behavior data set includes:

    A collection of high-frequency behavior data that matches the high-frequency behavior data under each behavior label with the user behavior data under the same behavior label, and the high-frequency attribute data under each attribute label matches the user attribute data under the same attribute label, Determined as the target high-frequency behavior data collection.
18. 18. The computer-readable storage medium of claim 17, wherein the program instructions, when executed by the processor, also cause the processor to execute:

    According to the user attribute data of the user under the designated attribute tag, determine the user's recommendation method information under different recommendation method columns, the recommendation method column includes a recommended font column, a recommended target image column, a recommended voice column or a recommended style of writing One or more of the columns;

    According to the recommendation method information, a target recommendation method of the user is determined, and the target recommendation method is recommended by the user in real time by the target recommendation information.
19. The device according to claim 16, wherein the target recommendation information includes a designated jump link; before the real-time recommendation of the target recommendation information to the user, the display interface of the first application includes the first A waiting image of a recommended object; the first recommended object is a virtual object that shows recommended information to the user; the waiting image is a display corresponding to the first recommended object when the target recommendation information is not determined image;

    When the program instructions are executed by the processor, the processor specifically executes:

    Generating an interactive image in which the first recommendation object recommends the target recommendation information according to the information text of the target recommendation information, and displaying the interactive image;

    In the case of receiving the user's confirmation interaction instruction for the interaction image, the instruction jump link in the target recommendation information is opened.
20. The computer-readable storage medium according to claim 16, wherein the program instructions, when executed by the processor, also cause the processor to execute:

    Acquiring historical operation data of multiple sample recommended users corresponding to each of the candidate recommendation information;

    Preprocessing the discrete numerical type sample behavior data in the historical operation data into continuous numerical segment type sample behavior data;

    Acquiring the sample behavior data under any at least one behavior label from the historical operation data of the sample recommended user corresponding to the candidate recommendation information after preprocessing, to form a candidate frequent item set corresponding to the candidate recommendation information;

    From the candidate frequent item set corresponding to the candidate recommendation information, determine the behavior data frequent item set corresponding to the candidate recommendation information; any sample behavior data in the behavior data frequent item set appears in the candidate recommendation information corresponding to the The number of times in the historical operation data of the sample recommended user is greater than the preset number of occurrence thresholds, and when the candidate frequent item set corresponding to the candidate recommendation information contains multiple sample behavior data, any number of frequent items in the behavior data set The number of times that the sample behavior data appear together in the historical operation data of the sample recommended users corresponding to the candidate recommendation information is greater than the preset number of occurrences threshold;

    According to the frequent item sets of behavior data corresponding to each candidate recommendation information, the high-frequency behavior data set corresponding to each candidate recommendation information is determined.

Images