CN110928913A - User display method, device, computer equipment and computer readable storage medium - Google Patents
User display method, device, computer equipment and computer readable storage medium Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The disclosure provides a user display method, a user display device, computer equipment and a computer readable storage medium, and belongs to the technical field of networks. The method comprises the following steps: according to the obtained user characteristics of the user logged in by the terminal, target sorting reference information matched with the user characteristics is obtained from the plurality of sorting reference information, the associated users of the user are sorted according to the target sorting reference information, and a sorting result is displayed. According to the method and the device, the target sorting reference information matched with the user sorting reference information is determined from the plurality of sorting reference information according to the characteristics of the user, the diversity of the sorting modes of the associated users can be improved, the associated users can be sorted according to the actual conditions of the user and the associated users, and the sorting accuracy of the associated users is improved.
Description
Technical Field
The present disclosure relates to the field of network technologies, and in particular, to a user display method and apparatus, a computer device, and a computer-readable storage medium.
Background
With the rapid development of computer technology and electronic products, mass leisure and entertainment gradually become diversified, and various types of game software, instant messaging software and the like emerge endlessly. In various types of software applications, users can select associated users who are displayed in a list form after being sorted, so that sharing of activity information or sending of activity invitation and the like can be achieved.
At present, associated users are sorted according to the sequence that the time interval between the occurrence time of the last conversation between the user and the associated user and the current time is from small to large, and the associated users are sequentially displayed on a visual interface in a list form according to the sorting result.
At present, the form of a method for sorting the associated users is too single, and the method cannot be changed according to the difference between the users and the associated users, so that the displayed associated users after sorting are not necessarily the users that the users want to view, and therefore, the accuracy of user display based on the sorting mode is low.
Disclosure of Invention
The embodiment of the disclosure provides a user display method, a user display device, computer equipment and a computer readable storage medium, which can solve the problems of single form and low accuracy of a user sorting method in the related technology. The technical scheme is as follows:
in one aspect, a user display method is provided, and the method includes:
when target operation is detected, user characteristics of a user logged in by a terminal are obtained, wherein the user characteristics comprise user attributes, associated user attributes and relationship attributes between the user and the associated user;
according to the user characteristics, acquiring target sorting reference information matched with the user characteristics from a plurality of sorting reference information, wherein different sorting reference information is used for indicating that the display sequence is determined based on different modes;
and sorting the associated users of the user according to the target sorting reference information, and displaying the associated users of the user according to a sorting result.
In one aspect, there is provided a user display apparatus, the apparatus comprising:
the system comprises a characteristic acquisition module, a characteristic acquisition module and a characteristic acquisition module, wherein the characteristic acquisition module is used for acquiring user characteristics of a user logged in by a terminal when target operation is detected, and the user characteristics comprise user attributes, associated user attributes and relationship attributes between the user and the associated user;
a reference information obtaining module, configured to obtain target ranking reference information that matches the user feature from a plurality of ranking reference information according to the user feature, where different ranking reference information is used to indicate that a display order is determined based on different manners;
the sorting module is used for sorting the associated users of the users according to the target sorting reference information;
and the display module is used for displaying the associated user of the user according to the sequencing result.
In one possible implementation, the feedback information includes:
based on the sending behavior of the exposed associated user.
In one possible implementation manner, determining, according to the feedback information of each sample user, target ranking reference information that matches the user characteristic of each sample user includes:
and determining the accuracy of the sequencing result of the associated users of the sample user according to the sending behavior of the sample user to the exposed associated users.
In one possible implementation, the feedback information further includes:
processing mode information of the shared content by the associated user as a sending target;
according to the feedback information of each sample user, after determining the target ranking reference information matched with the user characteristics of each sample user, the method further comprises the following steps:
and adjusting the target sorting reference information matched with the user characteristics of each sample user according to the processing mode information of the shared content by the associated user as the sending target.
In a possible implementation manner, the sorting module is further configured to sort, according to a random policy, associated users of the users logged in by the terminal in an order from small to large of the random numbers allocated to the users logged in by the terminal;
the sorting module is also used for sorting the associated users of the users logged in by the terminal according to a target feature sorting strategy and the sequence of the data values corresponding to the target features from high to low;
the device also includes:
the input module is used for inputting the user attribute, the associated user attribute and the relationship attribute between the user and the associated user into the machine learning model according to the machine learning algorithm sorting strategy;
the scoring module is used for scoring the associated user of the user logged in by the terminal through the machine learning model;
the sorting module is further used for sorting the associated users of the users logged in by the terminal according to the order of the scoring scores from high to low.
In one aspect, a computer device is provided that includes one or more processors and one or more memories having at least one program code stored therein, the program code being loaded and executed by the one or more processors to perform operations performed by the user display method.
In one aspect, a computer-readable storage medium having at least one program code stored therein is provided, the program code being loaded and executed by a processor to implement the operations performed by the user display method.
According to the obtained user characteristics of the user logged in by the terminal, target sorting reference information matched with the user characteristics is obtained from the plurality of sorting reference information, the associated users of the user are sorted according to the target sorting reference information, and a sorting result is displayed. By determining the target sorting reference information matched with the user characteristic from the plurality of sorting reference information according to the user characteristic, the diversity of the sorting modes of the associated users can be improved, the associated users can be sorted according to the actual conditions of the users and the associated users, and the sorting accuracy of the associated users is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic diagram of an implementation environment of a user display method provided by an embodiment of the present disclosure;
FIG. 2 is a basic flowchart of an experimental process for determining ranking reference information matching with user characteristics according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of an experimental process for determining ranking reference information matching user characteristics according to an embodiment of the present disclosure;
FIG. 4 is a flow chart of a user display method provided by an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a user display device provided in an embodiment of the present disclosure;
FIG. 6 is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a server according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present disclosure more apparent, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an implementation environment of a user display method provided in an embodiment of the present disclosure, and referring to fig. 1, the implementation environment includes: computer device 101 and server 102.
The computer device 101 may be at least one of a smartphone, a desktop computer, a tablet computer, an e-book reader, a laptop portable computer, and the like. The computer device 101 may be installed and run with a corresponding application program, e.g., a game application or the like. The computer device 101 may determine, according to the user characteristics of the logged-in user, ranking reference information matched with the logged-in user, and rank the associated users according to the ranking reference information, so that the user may select their associated users on the computer device, and implement sending related activity information to the selected associated users or sharing related content. For example, in a game application, a user may enable the computer device 101 to sort associated users in a list according to the sorting reference information by sharing operation on a game interface, and present a sorted result on a visual interface in a form of a list, so that the user selects an associated user on the list to share game information or send a game invitation, and the like.
The computer device 101 may be broadly referred to as one of a plurality of computer devices, and the embodiment is illustrated only with the computer device 101. Those skilled in the art will appreciate that the number of computer devices described above may be greater or fewer. For example, the number of the computer devices may be only a few, or the number of the computer devices may be several tens or hundreds, or more, and the number of the computer devices and the type of the devices are not limited in the embodiments of the present disclosure.
The server 102 may be at least one of a plurality of servers, a cloud computing platform, and a virtualization center, and the server 102 may obtain user characteristics of a plurality of sample users, determine different ranking reference information according to different user characteristics, and further process target ranking reference information matched with the user characteristics of each sample user according to feedback information of each sample user. Optionally, the number of the above servers may be more or less, and the embodiment of the disclosure does not limit this. Of course, the server 102 may also include other functional servers to provide more comprehensive and diverse services. Optionally, the server 102 may also maintain at least one sample user information database, such as a sample user attribute database, an associated user attribute database, a relational attribute database, and the like.
Fig. 2 is a basic flowchart of an experimental process for determining ranking reference information matching with a user feature according to an embodiment of the present disclosure, and referring to fig. 2, the experimental process may include: the method comprises the steps that a server acquires user characteristics and strategy sets of a large number of users, divides the acquired users into an exploration group and an application group, wherein the proportion of the number of users of the exploration group to the total number of the users is far smaller than the proportion of the number of users of the application group to the total number of the users, distributes ranking reference information to the users of the exploration group, namely, a ranking strategy, ranks related users for the users of the exploration group according to the distributed strategy, the users of the exploration group feed back ranking benefits based on ranking to the server through the operation of the related users on a terminal, the benefits can refer to whether the related users are sent, after the benefits are sent, whether the related users operate shared contents or not, and the like, when the benefits feedback meets target conditions, the ranking strategy that the benefits feedback meets the target conditions can be used as a ranking strategy matched with the user characteristics of the users of the exploration group, and distributing the sorting strategy to the application group users with the same or similar user characteristics with the exploration group users, and further adjusting the matching of the strategy based on the feedback of the application group users. The experimental process can be used as a training process of a decision system, and the process enables the finally determined sequencing strategy to achieve the effect optimization through a continuous iterative process of observing the user state of a user logged in by the terminal, executing the sequencing strategy, obtaining user feedback, adjusting the sequencing strategy and receiving the user feedback. Through the training process, a decision system for determining target ranking reference information can be obtained so as to perform ranking of associated users.
Fig. 2 is a basic flow of an experimental process for determining ranking reference information matched with a user feature according to an embodiment of the present disclosure, and a specific experimental process is described as an example below, referring to fig. 3, fig. 3 is a flow chart of an experimental process for determining ranking reference information matched with a user feature according to an embodiment of the present disclosure, referring to fig. 3, where the experimental process includes:
301. the server acquires user characteristics of a plurality of sample users and a plurality of sorting reference information, wherein the user characteristics comprise user attributes, associated user attributes and relationship attributes between the users and the associated users.
The plurality of sample users may be referred to as the search group users. The server may divide the acquired sample users into an exploration group and an application group at each fixed time, for example, when the user characteristics are acquired, when a plurality of pieces of ranking reference information are determined according to the user characteristics, or when the plurality of pieces of ranking reference information are distributed, where the grouping manner may be a random grouping manner or a grouping manner of other policies, and a specific time and a specific grouping manner for grouping are not limited in the embodiment of the present disclosure. Generally, the number of exploration group users accounts for a significantly smaller percentage of the total number of sample users than the number of application group users, for example, the number of exploration group users may account for 5% of the total number of sample users, and the number of application group users may account for 95% of the total number of sample users.
It should be noted that the server may obtain, based on the obtained user identifiers of the multiple sample users, user identifiers of the associated users on the associated user relationship chains of the multiple sample users, further obtain, based on the user identifiers of the sample users and the obtained user identifiers of the associated users, user profile information of the sample users and the associated users, perform feature extraction on the obtained user profile information, and obtain attributes of the sample users and the associated users, and further, the server may obtain, according to an interaction behavior of the user and the associated users, relationship attributes between the user and the associated users.
The associated user may be a user having an attention relationship, a subscription relationship, and a friend relationship with the sample user, and optionally, the associated user may also be a user having other possible relationships with the sample user, which is not limited in this disclosure.
In step 301, the user characteristic may include a user attribute, an associated user attribute, and a relationship attribute between the user and the associated user, where the user attribute may be a user identity information attribute, a user behavior information attribute, and the like, specifically, the user identity information attribute may be a gender, an age, an occupation, a geographic location, and the like of the user, and the user behavior information attribute may be a game playing characteristic (such as a game amount, a game duration, a game payment condition, and the like) of the user, and the like. The associated user attributes are similar to the user attributes and are not described in detail herein. The relationship attribute between the user and the associated user may be a relationship type, an interaction condition, a history invitation sending condition, and the like, and it should be noted that the user characteristic and the relationship attribute may further include other characteristics, which is not limited in this disclosure.
It should be noted that the sorting reference information may be a random strategy, a target feature sorting strategy, a machine learning algorithm sorting strategy, and the like, and optionally, the sorting reference information may also be other strategies, which is not limited in this disclosure.
302. The server assigns the plurality of ranking reference information to the sample user.
In one possible implementation manner, the server groups the exploration group users according to the user characteristics of the exploration group users to obtain a plurality of exploration subgroups, and randomly allocates the plurality of ranking reference information to the users in each exploration subgroup.
The server may determine a plurality of search subgroups and a group aggregate for each search subgroup based on the user characteristics of the search group users, and may determine the user characteristics corresponding to the group aggregate for each search subgroup as the user characteristics for each search subgroup. The above process can be realized by using a clustering algorithm, and the embodiment of the present disclosure does not limit which clustering algorithm is specifically used. For example, the server may group users into different numbers of activities as a group focus for each exploration subgroup, grouping users according to the number of activities entered by the users:
group A: entering into the activity for the first time;
group B: entering the activity again;
group C: entering the activity for multiple times;
……
the plurality of ranking reference information may be the ranking reference information after being screened in various policies or policy combinations in step 301. After the strategy is distributed to the sample users, the server can determine the sorting result of the associated users according to the sorting reference information and display the sorting result to the sample users. The determination process of the associated user ranking result may have the following four cases:
1. the server sorts the associated users of the sample users according to a random strategy and the sequence of the random numbers distributed by the server to the sample users from small to large;
2. the server sorts the associated users of the sample users according to a target feature sorting strategy and according to the sequence of the data values corresponding to the target features from high to low, for example, the associated users are sorted according to the sequence of the interaction times between the associated users and the sample users from large to small;
3. the server inputs the user attribute, the associated user attribute and the relationship attribute between the user and the associated user into a machine learning model according to a machine learning algorithm sorting strategy, scores the associated user of the sample user through the machine learning model, and sorts the associated user of the sample user according to the sequence of the scores from high to low;
4. and the server sorts the associated users of the sample users according to other strategies.
It should be noted that, the process of constructing the machine learning model may be: the server can obtain user attributes, associated user attributes and relationship attributes between the users and the associated users, convert the obtained attribute data into a vector form, and splice three vectors to construct a sample, so that the sample is used as part of training data of the machine learning model. Through the training data, the initial model is trained to obtain a machine learning model, which may be a logistic regression model, a decision tree model, a neural network model, or the like, and optionally, the machine learning model may also be another model, which is not limited in the embodiments of the present disclosure. The multiple machine learning models correspond to multiple sorting reference information, and associated users can be scored and sorted through different models.
303. And the server acquires feedback information of each sample user, wherein the feedback information is feedback information when the sample user displays the associated user based on the distributed sequencing reference information, and the feedback information comprises the sending behavior based on the exposed associated user.
In one possible implementation, the server performs statistics on the exposure and transmission data of the sample users to obtain feedback of different exploration subgroups under different strategies.
It should be noted that the feedback information may further include processing manner information of the shared content by the associated user as the sending target, for example, whether the associated user as the sending target registers an activity corresponding to the shared content, and the like, in a subsequent processing process, the server may adjust the target ranking reference information matched with the user characteristic according to the processing manner information of the shared content by the associated user as the sending target, and a specific adjustment process may refer to step 306.
304. And the server determines the accuracy of the sequencing result of the associated user according to the sending behavior based on the exposed associated user.
In a possible implementation manner, the server may detect the position of the exposed associated user in the list, determine whether the associated user as the sending target is located before the target position, and further determine the accuracy of the ranking result of the associated user according to the number of sample users located before the target position. For example, when M associated users are located before the target position among the N associated users as the transmission targets, it may be determined that the accuracy of the ranking result of the associated users is M/N.
Wherein N is the total number of associated users as a transmission destination, M is the number of associated users located before a destination position among the associated users as the transmission destination, M, M are positive integers greater than or equal to 1, and the value of M is less than or equal to the value of N. The target position may be any position, which is not limited in the embodiments of the present disclosure.
305. And when the accuracy of the associated user sorting result reaches a target threshold value, the server determines target sorting reference information matched with the user characteristics of each sample user.
It should be noted that the target threshold may be any preset value, and the specific value of the target threshold is not limited in the embodiment of the present disclosure.
In a possible implementation manner, the server may compare the accuracy of the ranking result of the associated user of each sample user with a preset target threshold, when the accuracy is higher than the target threshold, the currently-used reference ranking information may be used as candidate target ranking reference information matched with the user feature of the sample user, if there is only one candidate target ranking reference information, the candidate target ranking reference information may be directly determined as the target ranking reference information, if there are at least two candidate target ranking reference information, the server may compare the accuracy of the ranking result of the associated user of the at least two candidate target ranking reference information, and according to the comparison result, the candidate target ranking reference information with higher accuracy is used as the target ranking reference information matched with the user feature of the sample user.
It should be noted that, if the accuracy of the ranking results of the associated users of the at least two candidate target ranking reference information is the same, the specific processing mode of the shared content by the associated user serving as the sending target may be further determined and adjusted.
306. And the server adjusts the target sorting reference information matched with the user characteristics of each sample user according to the processing mode information of the shared content by the associated user as the sending target.
It should be noted that the processing manner information of the associated user as the sending target on the shared content may be a specific processing manner of the associated user as the sending target on the shared content, and the specific processing manner may be that the associated user ignores the shared content, the associated user browses the shared content but does not register, the associated user browses the shared content and registers, and the like.
In a possible implementation manner, the server may detect a specific processing manner of the shared content by the associated user as the sending target, and adjust the target ranking reference information matched with the user characteristics of each sample user according to a detected result. For example, the server determines two candidate target ranking reference information according to the accuracy of the ranking result of the associated user of the sample user, and determines that the accuracy of the two candidate target ranking reference information is the same, the server may further detect a specific processing manner of the shared content by the associated user serving as the sending target, and when it is detected that the number of associated users browsing the shared content and registering in one of the associated users serving as the sending target is large, the ranking reference information adopted by the sample user may be used as the target ranking reference information matched with the user characteristics of the sample user.
For example, the feedback situation of each search subgroup corresponding to the grouping manner in step 302 under different policies may be:
group a, strategy 1, expose xxx, send xxx, register xxx;
group a, strategy 2, expose xxx, send xxx, register xxx;
……
group C, strategy 1, expose xxx, send xxx, register xxx;
group C, strategy 2, expose xxx, send xxx, register xxx.
The server can determine the ranking reference information corresponding to each exploration subgroup according to the feedback information of the sample users corresponding to different strategies in each exploration subgroup and the specific processing mode of the shared content by the associated user serving as the sending target, and determine the ranking reference information corresponding to the exploration subgroup, which is obtained by sharing the activity content of the sample users and the ranking reference information with the most registered activity of the associated user serving as the sending target, as the ranking reference information matched with the user characteristics corresponding to the exploration subgroup.
Optionally, the server may further perform a digitization process on the processing manner information of the shared content by the associated user as the transmission target according to the specific processing manner of the shared content by the associated user as the transmission target, for example, the digitization process may be binarization or enumeration, taking binarization as an example, when the associated user browses and registers the shared content, the processing manner information of the associated user as the transmission target on the shared content is mapped to 1, when the associated user ignores the shared content, the processing manner information of the associated user as the transmission target on the shared content is mapped to 0, and further, the two kinds of data may be weighted according to a correct rate of a ranking result of the associated user, and target ranking reference information matching with the user characteristic of each sample user is adjusted according to a result of the weighted computation, the weights of the two data can be any values, and the specific weight value is not limited in the embodiment of the disclosure.
307. And the server distributes the ranking reference information for other users according to the target ranking reference information matched with the user characteristics of each sample user.
In a possible implementation manner, the server may group the application group users according to the states and characteristics of the application group users (similar to the above grouping manner of the group a, the group B, and the group C), and allocate ranking reference information to the application group users of each exploration subgroup according to the determined target ranking reference information matched with the user characteristics of each sample user.
When assigning the ranking reference information to the application group users of each search sub-group, the ranking reference information may be assigned according to the following probability:
wherein, Pr { AtA may represent the probability that the t-th group a user is assigned with the policy a, e is the base of the natural logarithm, a may represent the policy a, b may represent the policy b, k represents the total number of policies, Ht(a) May represent a benefit of executing the a policy, Ht(b) May represent a benefit, π, of executing the b-strategyt(a) May represent the probability of executing the a-policy.
It should be noted that the scheme provided by the embodiment of the present disclosure may be applied to a game application, where a server may use a game user registered in a game as a sample user, allocate a plurality of pieces of ranking reference information to the game user by obtaining user characteristics of a plurality of game users and the plurality of pieces of ranking reference information, and determine, according to feedback information of the game user, the ranking reference information matched with the user characteristics, so that the server allocates the ranking reference information to other users. Optionally, the scheme provided by the embodiment of the present disclosure may also be applied in other scenarios, which is not limited by the embodiment of the present disclosure.
The scheme provided by the embodiment of the disclosure constructs an intelligent strategy system through a machine learning method, namely, a real-time decision system, namely, a server constructs a plurality of sequencing models or rules through the behaviors of users, the behaviors of associated users and the relations between the behaviors, and explores different sequencing strategies through continuously splitting an exploration group from a user set to determine the current most suitable strategy, thereby allocating the optimal strategy to the appointed users according to the difference of target groups, and acquiring the feedback of various strategies in real time to adjust the sequencing strategies matched with different user characteristics in real time, namely, sequencing reference information, and determining the sequencing strategies matched with various user characteristics according to the experiment and feedback conditions of users of the exploration group, so that the server can allocate the corresponding strategies to the users of the application group according to the characteristics of the users of the application group, ranking of associated users of the application group users is achieved.
According to the scheme provided by the embodiment of the disclosure, a plurality of pieces of sequencing information are determined according to the user characteristics of a plurality of sample users, the sequencing information is distributed to the corresponding sample users, the accuracy of the sequencing result of the associated user of the sample user is determined according to the feedback information of each sample user, the sequencing reference information of which the accuracy reaches a target threshold is determined as the target sequencing reference information matched with the user characteristics of each sample user, the target sequencing reference information matched with the user characteristics of each sample user can be adjusted according to the specific processing mode of the associated user as a sending target on the shared content, the matching of the sequencing reference information and the user characteristics can be realized through the experimental process, so that the target sequencing reference information matched with the user characteristics can be determined from the sequencing reference information according to the user characteristics of each user, the method and the device for sorting the associated users can improve the diversity of the sorting modes of the associated users, can sort the associated users according to the actual conditions of the users and the associated users, and improve the sorting accuracy of the associated users.
Fig. 3 is a process in which the server determines ranking reference information matched with different user characteristics through an experimental process, and a process in which the server determines ranking reference information matched with the user characteristics of the user logged in by the terminal based on the user characteristics of the user and ranks the associated user of the user is described as an example, referring to fig. 4, where fig. 4 is a flowchart of a user display method provided in an embodiment of the present disclosure, and the method includes:
401. when the computer device detects the target operation, user characteristics of a user logged in by the terminal are obtained, wherein the user characteristics comprise user attributes, associated user attributes and relationship attributes between the user and the associated user.
It should be noted that the target operation may be a sharing operation triggered by a user on a computer device, and optionally, the target operation may also be another operation, which is not limited in this disclosure.
In a possible implementation manner, the computer device may detect, in real time, an operation triggered on the computer device, and when it is detected that the target operation is triggered, the computer device may obtain a user characteristic of the user logged in the computer device, where the user characteristic is similar to the user characteristic in step 301, and details are not repeated here.
402. The computer device acquires target sorting reference information matched with the user characteristic from a plurality of sorting reference information according to the user characteristic, and different sorting reference information is used for indicating that the display sequence is determined based on different modes.
In a possible implementation manner, the computer device may detect, according to the obtained user feature, similarity between the user feature and each group centroid, compare the detected similarity values, determine, according to a comparison result, a group centroid closest to the user feature, and use ranking reference information corresponding to the group centroid as target ranking reference information matched with the user feature.
It should be noted that the sorting reference information may be a random strategy, a target feature sorting strategy, a machine learning algorithm sorting strategy, and the like, and optionally, the sorting reference information may also be other strategies, which is not limited in this disclosure.
403. And the computer equipment sorts the associated users of the user according to the target sorting reference information and displays the associated users of the user according to a sorting result.
It should be noted that, the computer device may have the following specific implementation manners for sorting the associated users of the user according to the target sorting information:
1. the computer equipment sorts the associated users of the users logged in by the terminal according to the random strategy and the sequence of the random numbers distributed by the computer equipment to the users logged in by the terminal from small to large;
2. the computer equipment sorts the associated users of the users logged in by the terminal according to the target feature sorting strategy and the sequence of the data values corresponding to the target features from high to low;
3. the computer equipment inputs the user attribute of the user logged in by the terminal, the associated user attribute and the relationship attribute between the user and the associated user into a machine learning model according to a machine learning algorithm sequencing strategy, scores the associated user of the user logged in by the terminal through the machine learning model, and sequences the associated user of the user logged in by the terminal according to the sequence of the scores from high to low;
4. and the computer equipment sorts the associated users of the users logged in by the terminal according to other strategies.
In a possible implementation manner, after the computer device sorts the associated users of the user logged in by the terminal according to the target sorting reference information in any one of the four manners, the associated users of the user may be displayed according to a sorting result, so that the user can select the associated users.
It should be noted that the scheme provided by the embodiment of the present disclosure may be applied to a non-associated scenario to rank associated users of users logged in by a terminal, where the non-associated scenario may be a scenario that does not need to consider an influence between each step of action. For example, the scheme provided by the embodiment of the present disclosure may be applied to game applications, and a user may perform a sharing operation on a game interface to enable a computer device to determine corresponding ranking reference information according to a user characteristic, rank associated users in a list according to the determined ranking reference information, and display a ranked result, so that the user may visually view a ranking result of the associated user, and select the associated user to share game information or send a game invitation, and the like. Optionally, the scheme provided by the embodiment of the present disclosure may also be applied to other possible scenarios, which are not limited by the embodiment of the present disclosure.
According to the scheme provided by the embodiment of the disclosure, the target sorting reference information matched with the user characteristics is acquired from the plurality of sorting reference information according to the acquired user characteristics of the user logged in by the terminal, the associated users of the user are sorted according to the target sorting reference information, and the sorting result is displayed. According to the user characteristics, the target ranking reference information matched with the user characteristics is determined from the plurality of ranking reference information, the diversity of the ranking modes of the associated users can be improved, the associated users can be ranked according to the actual conditions of the users and the associated users, and the ranking accuracy of the associated users is improved.
All the above optional technical solutions may be combined arbitrarily to form the optional embodiments of the present disclosure, and are not described herein again.
Fig. 5 is a schematic structural diagram of a user display device provided in an embodiment of the present disclosure, and referring to fig. 5, the device includes:
a feature obtaining module 501, configured to, when a target operation is detected, obtain user features of a user logged in by a terminal, where the user features include a user attribute, an associated user attribute, and a relationship attribute between the user and the associated user;
a reference information obtaining module 502, configured to obtain target ranking reference information that matches the user characteristic from a plurality of ranking reference information according to the user characteristic, where different ranking reference information is used to indicate that a display order is determined based on different manners;
a sorting module 503, configured to sort, according to the target sorting reference information, the associated users of the user;
and a display module 504, configured to display the associated user of the user according to the sorting result.
The device acquires target sorting reference information matched with the user characteristics from the plurality of sorting reference information according to the acquired user characteristics of the user logged in by the terminal, sorts the associated users of the user according to the target sorting reference information, and displays the sorting result. By determining the target sorting reference information matched with the user characteristic from the plurality of sorting reference information according to the user characteristic, the diversity of the sorting modes of the associated users can be improved, and the sorting accuracy of the associated users can be improved by sorting the associated users according to the actual conditions of the users and the associated users.
In one possible implementation manner, the determining process of the target ranking reference information matched with the user feature includes:
acquiring user characteristics and a plurality of sequencing reference information of a plurality of sample users;
assigning the plurality of ranking reference information to the sample user;
obtaining feedback information of each sample user, wherein the feedback information is feedback information when the sample user displays the associated user based on the distributed sequencing reference information;
and determining target ranking reference information matched with the user characteristics of each sample user according to the feedback information of each sample user.
In one possible implementation, the feedback information includes: based on the sending behavior of the exposed associated user.
In a possible implementation manner, the determining, according to the feedback information of each of the sample users, target ranking reference information that matches the user characteristics of each of the sample users includes:
and determining the accuracy of the sequencing result of the associated users of the sample user according to the sending behavior of the sample user to the exposed associated users.
In one possible implementation, the feedback information further includes:
processing mode information of the shared content by the associated user as a sending target;
according to the feedback information of each sample user, after determining the target ranking reference information matched with the user characteristics of each sample user, the method further comprises the following steps:
and adjusting the target sorting reference information matched with the user characteristics of each sample user according to the processing mode information of the shared content by the associated user as the sending target.
In a possible implementation manner, the sorting module 503 is further configured to sort, according to a random policy, associated users of the users logged in by the terminal according to a sequence from a small random number to a large random number allocated to the users logged in by the terminal;
the sorting module 503 is further configured to sort, according to a target feature sorting policy, associated users of the users logged in by the terminal in an order from high to low of data values corresponding to the target features;
the device also includes:
the input module is used for inputting the user attribute, the associated user attribute and the relationship attribute between the user and the associated user into the machine learning model according to the machine learning algorithm sorting strategy;
the scoring module is used for scoring the associated user of the user logged in by the terminal through the machine learning model;
the sorting module 503 is further configured to sort, according to the order from high to low of the scoring score, the associated users of the users logged in by the terminal.
It should be noted that: in the user display device provided in the above embodiment, when displaying a user, only the division of the above functional modules is exemplified, and in practical applications, the above functions may be distributed by different functional modules according to needs, that is, the internal structure of the computer device may be divided into different functional modules to complete all or part of the above described functions. In addition, the user display device and the user display method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
Fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure. The computer device 600 may be: a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer iv, motion video Experts compression standard Audio Layer 4), a notebook computer, or a desktop computer. Computer device 600 may also be referred to by other names such as user device, portable computer device, laptop computer device, desktop computer device, and so forth.
Generally, the computer device 600 includes: one or more processors 601 and one or more memories 602.
The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 601 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 601 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 601 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, processor 601 may also include an AI (Artificial Intelligence) processor for processing computational operations related to machine learning.
The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in the memory 602 is used to store at least one program code for execution by the processor 601 to implement the user display method provided by the method embodiments in the present disclosure.
In some embodiments, the computer device 600 may further optionally include: a peripheral interface 603 and at least one peripheral. The processor 601, memory 602, and peripheral interface 603 may be connected by buses or signal lines. Various peripheral devices may be connected to the peripheral interface 603 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 604, a display 605, a camera 606, an audio circuit 607, a positioning component 608, and a power supply 609.
The peripheral interface 603 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 601 and the memory 602. In some embodiments, the processor 601, memory 602, and peripheral interface 603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 601, the memory 602, and the peripheral interface 603 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.
The Radio Frequency circuit 604 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 604 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 604 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 604 comprises: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 604 may communicate with other computer devices via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the radio frequency circuit 604 may also include NFC (Near Field Communication) related circuits, which are not limited by this disclosure.
The display 605 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 605 is a touch display screen, the display screen 605 also has the ability to capture touch signals on or over the surface of the display screen 605. The touch signal may be input to the processor 601 as a control signal for processing. At this point, the display 605 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 605 may be one, providing the front panel of the computer device 600; in other embodiments, the display 605 may be at least two, respectively disposed on different surfaces of the computer device 600 or in a folded design; in still other embodiments, the display 605 may be a flexible display disposed on a curved surface or on a folded surface of the computer device 600. Even more, the display 605 may be arranged in a non-rectangular irregular pattern, i.e., a shaped screen. The Display 605 may be made of LCD (Liquid Crystal Display), OLED (Organic Light-emitting diode), and the like.
The camera assembly 606 is used to capture images or video. Optionally, camera assembly 606 includes a front camera and a rear camera. Generally, a front camera is disposed on a front panel of a computer apparatus, and a rear camera is disposed on a rear surface of the computer apparatus. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 606 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.
The Location component 608 is used to locate the current geographic Location of the computer device 600 to implement navigation or LBS (Location Based Service). The positioning component 608 can be a positioning component based on the GPS (global positioning System) in the united states, the beidou System in china, the graves System in russia, or the galileo System in the european union.
The power supply 609 is used to supply power to the various components in the computer device 600. The power supply 609 may be ac, dc, disposable or rechargeable. When the power supply 609 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.
In some embodiments, the computer device 600 also includes one or more sensors 610. The one or more sensors 610 include, but are not limited to: acceleration sensor 611, gyro sensor 612, pressure sensor 613, fingerprint sensor 614, optical sensor 615, and proximity sensor 616.
The acceleration sensor 611 may detect the magnitude of acceleration in three coordinate axes of a coordinate system established with the computer apparatus 600. For example, the acceleration sensor 611 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 601 may control the display screen 605 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 611. The acceleration sensor 611 may also be used for acquisition of motion data of a game or a user.
The gyro sensor 612 may detect a body direction and a rotation angle of the computer apparatus 600, and the gyro sensor 612 may cooperate with the acceleration sensor 611 to acquire a 3D motion of the user on the computer apparatus 600. The processor 601 may implement the following functions according to the data collected by the gyro sensor 612: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.
The pressure sensor 613 may be disposed on a side bezel of the computer device 600 and/or underneath the display screen 605. When the pressure sensor 613 is disposed on the side frame of the computer device 600, the holding signal of the user to the computer device 600 can be detected, and the processor 601 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 613. When the pressure sensor 613 is disposed at the lower layer of the display screen 605, the processor 601 controls the operability control on the UI interface according to the pressure operation of the user on the display screen 605. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.
The fingerprint sensor 614 is used for collecting a fingerprint of a user, and the processor 601 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 614, or the fingerprint sensor 614 identifies the identity of the user according to the collected fingerprint. Upon identifying that the user's identity is a trusted identity, the processor 601 authorizes the user to perform relevant sensitive operations including unlocking the screen, viewing encrypted information, downloading software, paying, and changing settings, etc. The fingerprint sensor 614 may be provided on the front, back, or side of the computer device 600. When a physical key or vendor Logo is provided on the computer device 600, the fingerprint sensor 614 may be integrated with the physical key or vendor Logo.
The optical sensor 615 is used to collect the ambient light intensity. In one embodiment, processor 601 may control the display brightness of display screen 605 based on the ambient light intensity collected by optical sensor 615. Specifically, when the ambient light intensity is high, the display brightness of the display screen 605 is increased; when the ambient light intensity is low, the display brightness of the display screen 605 is adjusted down. In another embodiment, the processor 601 may also dynamically adjust the shooting parameters of the camera assembly 606 according to the ambient light intensity collected by the optical sensor 615.
The proximity sensor 616, also known as a distance sensor, is typically disposed on the front panel of the computer device 600. The proximity sensor 616 is used to capture the distance between the user and the front of the computer device 600. In one embodiment, the processor 601 controls the display screen 605 to switch from the bright screen state to the dark screen state when the proximity sensor 616 detects that the distance between the user and the front face of the computer device 600 is gradually decreased; when the proximity sensor 616 detects that the distance between the user and the front of the computer device 600 is gradually increasing, the display screen 605 is controlled by the processor 601 to switch from the breath-screen state to the bright-screen state.
Those skilled in the art will appreciate that the configuration shown in FIG. 6 does not constitute a limitation of the computer device 600, and may include more or fewer components than those shown, or combine certain components, or employ a different arrangement of components.
Fig. 7 is a schematic structural diagram of a server 700 according to an embodiment of the present disclosure, where the server 700 may generate a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 701 and one or more memories 702, where at least one program code is stored in the one or more memories 702, and is loaded and executed by the one or more processors 701 to implement the methods provided by the foregoing method embodiments. Of course, the server 700 may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input and output, and the server 700 may also include other components for implementing the functions of the device, which are not described herein again.
In an exemplary embodiment, a computer readable storage medium, such as a memory including program code, executable by a processor, to perform the user display method in the above embodiments is also provided. For example, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a compact disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by hardware associated with program code, and the program may be stored in a computer readable storage medium, where the above mentioned storage medium may be a read-only memory, a magnetic or optical disk, etc.
The foregoing is considered as illustrative of the embodiments of the disclosure and is not to be construed as limiting thereof, and any modifications, equivalents, improvements and the like made within the spirit and principle of the disclosure are intended to be included within the scope of the disclosure.
Claims (10)
1. A method for user display, the method comprising:
when target operation is detected, user characteristics of a user logged in by a terminal are obtained, wherein the user characteristics comprise user attributes, associated user attributes and relationship attributes between the user and the associated user;
according to the user characteristics, target sorting reference information matched with the user characteristics is obtained from a plurality of sorting reference information, and different sorting reference information is used for indicating that the display sequence is determined based on different modes;
and sorting the associated users of the users according to the target sorting reference information, and displaying the associated users of the users according to a sorting result.
2. The method according to claim 1, wherein the determining of the target ranking reference information matching the user characteristic comprises:
acquiring user characteristics and a plurality of sequencing reference information of a plurality of sample users;
assigning the plurality of ranking reference information to the sample user;
obtaining feedback information of each sample user, wherein the feedback information is feedback information of the sample user when the sample user displays the associated user based on the distributed sequencing reference information;
and determining target sorting reference information matched with the user characteristics of each sample user according to the feedback information of each sample user.
3. The method of claim 2, wherein the feedback information comprises: based on the sending behavior of the exposed associated user.
4. The method according to claim 3, wherein the determining the target ranking reference information matching the user characteristics of each of the sample users according to the feedback information of each of the sample users comprises:
and determining the accuracy of the sequencing result of the associated users of the sample user according to the sending behavior of the sample user to the exposed associated users.
5. The method of claim 2, wherein the feedback information further comprises:
processing mode information of the shared content by the associated user as a sending target;
after determining the target ranking reference information matched with the user characteristics of each sample user according to the feedback information of each sample user, the method further comprises:
and adjusting the target sorting reference information matched with the user characteristics of each sample user according to the processing mode information of the shared content by the associated user as the sending target.
6. The method of claim 1, wherein the ranking the associated ones of the users according to the target ranking reference information comprises:
according to a random strategy, sequencing the associated users of the users logged in by the terminal according to the sequence of the random numbers distributed to the users logged in by the terminal from small to large;
according to a target feature sorting strategy, sorting the associated users of the users logged in by the terminal according to the sequence of the data values corresponding to the target features from high to low;
and inputting the user attribute, the associated user attribute and the relationship attribute between the user and the associated user into a machine learning model according to a machine learning algorithm sorting strategy, scoring the associated user of the user logged in the terminal through the machine learning model, and sorting the associated user of the user logged in the terminal according to the sequence of the scoring scores from high to low.
7. A user display device, the device comprising:
the system comprises a characteristic acquisition module, a characteristic acquisition module and a characteristic acquisition module, wherein the characteristic acquisition module is used for acquiring user characteristics of a user logged in by a terminal when target operation is detected, and the user characteristics comprise user attributes, associated user attributes and relationship attributes between the user and the associated user;
the reference information acquisition module is used for acquiring target sorting reference information matched with the user characteristics from a plurality of sorting reference information according to the user characteristics, and different sorting reference information is used for indicating that the display sequence is determined based on different modes;
the sorting module is used for sorting the associated users of the users according to the target sorting reference information;
and the display module is used for displaying the associated users of the users according to the sequencing result.
8. The apparatus of claim 7, wherein the determining of the target ranking reference information matching the user characteristic comprises:
acquiring user characteristics and a plurality of sequencing reference information of a plurality of sample users;
assigning the plurality of ranking reference information to the sample user;
obtaining feedback information of each sample user, wherein the feedback information is feedback information of the sample user when the sample user displays the associated user based on the distributed sequencing reference information;
and determining target sorting reference information matched with the user characteristics of each sample user according to the feedback information of each sample user.
9. A computer device comprising one or more processors and one or more memories having at least one program code stored therein, the program code loaded and executed by the one or more processors to implement the operations performed by the user display method of any one of claims 1 to 6.
10. A computer-readable storage medium having at least one program code stored therein, the program code being loaded and executed by a processor to implement the operations performed by the user display method of any one of claims 1 to 6.
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