CN111681049A

CN111681049A - User behavior processing method, storage medium and related equipment

Info

Publication number: CN111681049A
Application number: CN202010502128.3A
Authority: CN
Inventors: 黄昕虹
Original assignee: Guangzhou Shiyuan Electronics Thecnology Co Ltd
Current assignee: Guangzhou Shiyuan Electronics Thecnology Co Ltd
Priority date: 2020-06-04
Filing date: 2020-06-04
Publication date: 2020-09-18
Anticipated expiration: 2040-06-04
Also published as: CN111681049B

Abstract

The embodiment of the application discloses a user behavior processing method, a storage medium and related equipment, wherein the method comprises the following steps: acquiring behavior data of a plurality of objects; constructing a directed graph based on the behavior data of the plurality of objects, wherein the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge; and searching the directed graph, and determining at least one behavior path for transferring the non-target behavior to the target behavior and the weight value of each behavior path, wherein the number of different behaviors contained in the behavior paths is less than or equal to the preset number. Therefore, the technical effects of improving the prediction accuracy, improving the user experience and improving the user stickiness can be achieved, and the technical problems that the processing accuracy of the user behaviors is not high and the user experience is poor in the related technology are solved.

Description

User behavior processing method, storage medium and related equipment

Technical Field

The present application relates to the field of internet, and in particular, to a user behavior processing method, a storage medium, and a related device.

Background

Currently, user behavior analysis and behavior prediction usually predict whether a user will perform a specific behavior by extracting user features and using a classification model, for example, in an e-commerce shopping scenario, the purchasing potential of a user-commodity pair can be captured by extracting features, and the specific implementation manner is as follows: extracting and applying a Gradient Boosting Decision Tree (GBDT) as a training model; a user prediction model is constructed by using a mathematical and statistical model in combination with theories such as user purchasing behaviors, processes, influence factors and the like; constructing a selection model based on potential factors based on the target behavior sequence data, and further predicting a purchase decision of a user in a purchase period; and (3) predicting the probability of multiple purchases of the customer in a certain time period by adopting mathematical models such as linear regression and logarithm models.

However, in an actual scene, due to the fact that the iteration speed of the APP function is high, the change frequency of the behavior habit of the user is high, the difficulty of user behavior prediction is increased, and whether the user performs a specific behavior is predicted only based on the modeling of a single user characteristic, so that the user behavior prediction accuracy is low, and the user experience is poor.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the application provides a user behavior processing method, a storage medium and related equipment, so as to solve the technical problems that in the related technology, the accuracy of user behavior prediction is not high, and the user experience is poor.

According to an aspect of an embodiment of the present application, a method for processing user behavior is provided, including: acquiring behavior data of a plurality of objects; constructing a directed graph based on the behavior data of the plurality of objects, wherein the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge; and searching the directed graph, and determining at least one behavior path for transferring the non-target behavior to the target behavior and the weight value of each behavior path, wherein the number of different behaviors contained in the behavior paths is less than or equal to the preset number.

Optionally, constructing the directed graph based on the behavior data of the plurality of objects includes: segmenting the behavior data of a plurality of objects to obtain a plurality of behavior sequences; constructing a training set and a testing set based on a plurality of behavior sequences, wherein the training set comprises: a positive sample training set and a negative sample training set, the test set comprising: the method comprises the steps of a positive sample test set and a negative sample test set, wherein the positive sample is a behavior sequence containing a target behavior, and the negative sample is a behavior sequence not containing the target behavior; constructing a first directed graph by utilizing a training set; testing the first directed graph based on the test set to obtain a test result; and under the condition that the test result does not meet the preset condition, updating the first directed graph until the test result meets the preset condition to obtain the directed graph.

Optionally, constructing the initial directed graph by using the training set includes: constructing a second directed graph by using the positive sample training set; determining a first behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the positive sample edge shearing rate; determining a second behavior sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the negative sample edge shearing rate; and based on the first behavior sequence and the second behavior sequence, performing edge clipping on the second directed graph to obtain the first directed graph.

Optionally, the updating the first directed graph includes: updating the positive sample edge shearing rate and the negative sample edge shearing rate based on a preset step length to obtain the updated positive sample edge shearing rate and the updated negative sample edge shearing rate; determining a third behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the updated positive sample edge shearing rate; determining a fourth line sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the updated negative sample edge shearing rate; and trimming the second directed graph based on the third behavior sequence and the fourth behavior sequence to obtain a third directed graph, wherein the third directed graph is determined to be the directed graph under the condition that the test result corresponding to the second directed graph meets the preset condition.

Optionally, constructing the second directed graph by using the positive sample training set includes: processing the positive sample training set, and determining nodes and edges in the second directed graph; performing characteristic extraction on the behavior data of the two behaviors connected with the edge to obtain characteristic data corresponding to the edge; and obtaining the weight value of the edge based on the characteristic data corresponding to the edge.

Optionally, constructing the training set and the test set based on the plurality of behavior sequences includes: determining a positive sample data set and a negative sample data set based on the plurality of behavior sequences; randomly dividing the positive sample data set to obtain a positive sample training set and a positive sample testing set; and randomly dividing the negative sample data set to obtain a negative sample training set and a negative sample testing set.

Optionally, the determining whether the test result meets the preset condition includes: determining a first recognition rate based on a first test result of the positive sample test set, wherein the first recognition rate is used for representing the proportion of the positive sample test result in the first test result; determining a second recognition rate based on a second test result of the negative sample test set, wherein the second recognition rate is used for representing the proportion of the positive sample test result in the second test result; after the positive sample edge shearing rate and the negative sample edge shearing rate are updated, acquiring a first variable quantity of the first identification rate and a second variable quantity of the second identification rate; judging whether the first recognition rate is smaller than a preset value, whether the first recognition rate is smaller than a second recognition rate and whether the first variation is smaller than the second variation; if the first identification rate is smaller than a preset value, the first identification rate is smaller than the second identification rate, and the first variation is smaller than the second variation, determining that the test result meets a preset condition; and if the first identification rate is greater than or equal to a preset value, the first identification rate is greater than or equal to a second identification rate, or the first variation is greater than or equal to a second variation, determining that the test result does not meet the preset condition.

Optionally, before the behavior data of the plurality of objects is segmented to obtain a plurality of behavior sequences, the method further includes: matching the behavior data with preset behavior data; if the behavior data is successfully matched with the preset behavior data, the behavior data is removed to obtain cleaned behavior data; and segmenting the cleaned behavior data to obtain a plurality of behavior sequences.

Optionally, before constructing the training set and the test set based on the plurality of behavior sequences, the method further includes: judging whether two adjacent behavior sequences are the same; if the two adjacent behavior sequences are the same, removing any one behavior sequence from the two adjacent behavior sequences to obtain a cleaned behavior sequence; and constructing a training set and a testing set based on the cleaned behavior sequence.

Optionally, after matching the target behavior sequence with at least one behavior path and determining a target behavior path, the method further includes: acquiring a target behavior sequence of a target object; matching the target behavior sequence with at least one behavior path to determine a target behavior path; acquiring a weighted value of a target behavior path; and determining whether to push target information based on the weight value of the target behavior path, wherein the target information is used for prompting the target object to execute the target behavior.

Optionally, after determining the push target information, the method further includes: monitoring whether a target object executes a target behavior; and updating the directed graph based on the target behavior sequence under the condition that the target object does not execute the target behavior.

Optionally, before matching the target behavior sequence with at least one behavior path and determining a target behavior path, the method further includes: matching the target behavior data of the target behavior sequence with preset behavior data; if the target behavior data is successfully matched with the preset behavior data, the target behavior data is removed, and a cleaned target behavior sequence is obtained; and matching the cleaned target behavior sequence with at least one behavior path to determine a target behavior path.

According to an aspect of an embodiment of the present application, a method for processing user behavior is provided, including: acquiring a target behavior sequence of a target object; matching the target behavior sequence with at least one behavior path to determine a target behavior path, wherein the at least one behavior path is a behavior path for transferring a non-target behavior determined by retrieving a directed graph to the target behavior, the number of different behaviors contained in the behavior path is less than or equal to a preset number, the directed graph is constructed based on behavior data of a plurality of objects, and the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge; acquiring a weight value of a target behavior path, wherein the weight value is determined by retrieving a directed graph; and determining whether to push target information based on the weight value of the target behavior path, wherein the target information is used for prompting the target object to execute the target behavior.

According to another aspect of embodiments of the present application, there is also provided a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to carry out the above-mentioned method steps.

According to another aspect of the embodiments of the present application, there is also provided an electronic device, including: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.

In the embodiment of the application, a directed graph is constructed based on behavior data of a plurality of objects, the directed graph is retrieved, at least one behavior path for transferring a non-target behavior to a target behavior is determined, a target behavior sequence of the target object is further matched with the at least one behavior path, and the target behavior path can be determined, so that the purpose of constructing a entitled directed graph by using historical behavior data of the whole user group and predicting the future behavior path of a user through the entitled directed graph is achieved, the technical effects of improving the prediction accuracy, improving the user experience and improving the user stickiness are achieved, and the technical problems that the accuracy of user behavior prediction in the related technology is not high and the user experience is poor are solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a flow chart of a first method for processing user behavior according to an embodiment of the present application;

FIG. 2 is a flow chart of a second method for processing user behavior according to an embodiment of the present application;

FIG. 3 is a flow chart of a third method for processing user behavior according to an embodiment of the present application;

FIG. 4 is a flow chart of a fourth method for processing user behavior according to an embodiment of the present application;

FIG. 5 is a flow chart of an alternative engine terminal according to an embodiment of the present application;

FIG. 6 is a flow chart of an alternative engine-side construction dataset according to an embodiment of the present application;

FIG. 7 is a flow chart of an alternative server side according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a device for processing user behavior according to an embodiment of the present application;

FIG. 9 is a schematic diagram of another user behavior processing device according to an embodiment of the application; and

fig. 10 is a schematic diagram of an electronic device according to an embodiment of the application.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

Example 1

The embodiment of the present application provides a method for processing a user behavior, where the method is applied to an electronic device, such as a computer terminal, a smart phone, a tablet computer, and the like, but is not limited thereto, and the method is not limited in this application.

Fig. 1 is a flowchart of a first user behavior processing method according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:

step S102, acquiring behavior data of a plurality of objects;

the object in the above step may be a user using an APP installed on the electronic device, and the APP may be an internet APP, for example, but not limited to, a class optimization teacher APP.

Different users usually perform different behavior operations when using the same APP, and in order to accurately predict the user behavior of each user, historical behavior data of all users in the APP may be obtained, for example, historical behavior data of a week of all users may be obtained. In order to obtain historical behavior data of a user, the behavior data of the user can be recorded in real time in the process that the user uses the APP, and the behavior data is stored in the specific database based on the operation time, so that the behavior data in a specific time period can be inquired from the specific database as required.

Step S104, constructing a directed graph based on the behavior data of the plurality of objects, wherein the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge;

in this embodiment, in order to predict a future behavior path of a user, an authorized directed graph may be constructed by using historical behavior data of the user, where different behaviors of the user may be represented by different nodes, and a transition relationship between behaviors corresponding to the nodes may be represented by edges directly connected to the nodes.

Further, the weight values of the edges can be calculated by using the characteristics of the number of users of the behavior pairs, the occurrence frequency of the behavior pairs, the number of users of the behavior nodes transferred to other nodes, the occurrence frequency of the behavior nodes and the like, and the weight values of the edges are used for representing the importance degree of the transfer relationship between the nodes, wherein the more important the transfer between the nodes is, the larger the weight value of the corresponding edge is, and the more users have the behavior.

And step S106, retrieving the directed graph, and determining at least one behavior path for transferring the non-target behavior to the target behavior and the weight value of each behavior path, wherein the number of different behaviors contained in the behavior path is less than or equal to the preset number.

The target behavior in the above steps may be a predefined key behavior, for example, a function that is less used in APP but is desired to be contacted by the operator, and the non-target behavior may refer to other behaviors besides the key behavior, for example, taking class optimization master APP as an example, and the target behavior may be a function of using a publishing job in the broadcasting station, but is not limited thereto. The preset number may be determined according to the prediction requirement of the user behavior in different application scenarios, which is not specifically limited in the present invention.

In an exemplary embodiment of the present invention, a directed graph may be searched to obtain paths from all non-target behaviors to a target behavior within a certain step number, and a weight value of the path may be obtained by obtaining a sum of weight values of all edges in the path.

In the embodiment of the application, the directed graph is constructed based on the behavior data of the objects, the directed graph is retrieved, and at least one behavior path for transferring the non-target behavior to the target behavior is determined, so that the purpose of constructing the authorized directed graph by using the historical behavior data of the whole user group is achieved, the prediction accuracy is improved, the user experience is improved, the technical effect of user stickiness is improved, and the technical problems that the accuracy of user behavior prediction is not high and the user experience is poor in the related technology are solved.

Example 2

As shown in fig. 2, the method comprises the steps of:

step S201, acquiring behavior data of a plurality of objects;

step S202, matching the behavior data with preset behavior data;

the preset behavior data in the above steps may be predefined noise behaviors and taint, for example, taking a class optimization master APP as an example, the noise behaviors may include but are not limited to: modifying nicknames, setting head portraits, etc.

Step S203, if the behavior data is successfully matched with the preset behavior data, the behavior data is removed to obtain cleaned behavior data;

in an exemplary embodiment of the present invention, after obtaining historical behavior data of a full number of users, the obtained historical behavior data may be cleaned based on preset behavior data to obtain clean behavior data.

Step S204, dividing the cleaned behavior data to obtain a plurality of behavior sequences;

in an exemplary embodiment of the present invention, a rule for behavior data segmentation may be determined based on factors such as APP usage scenarios and user behavior habits, and historical behavior data of the same user is segmented according to the rule to obtain a behavior sequence. For example, the rule may be that the user does not operate for 10 minutes, and then the user is determined to start a new sequence of actions.

Step S205, judging whether two adjacent behavior sequences are the same;

in an exemplary embodiment of the present invention, since a user often performs a repetitive operation during using an APP, after segmenting behavior data, a repetitive behavior sequence may be obtained, and therefore, an adjacent repetitive behavior sequence needs to be cleaned.

It should be noted that the repeated click behavior of the user can be cleaned, so as to solve the problem that the behavior log is repeatedly reported.

Step S206, if the two adjacent behavior sequences are the same, any one behavior sequence in the two adjacent behavior sequences is removed to obtain a cleaned behavior sequence;

in an exemplary embodiment of the present invention, for adjacent repeated behavior sequences, only one of the behavior sequences may be retained, and other repeated behavior sequences are removed to obtain a clean behavior sequence.

Step S207, constructing a training set and a testing set based on the cleaned behavior sequence, wherein the training set comprises: a positive sample training set and a negative sample training set, the test set comprising: the method comprises the steps of a positive sample test set and a negative sample test set, wherein the positive sample is a behavior sequence containing a target behavior, and the negative sample is a behavior sequence not containing the target behavior;

the training set in the above steps is used for constructing a directed graph, and the test set is used for testing the directed graph, determining whether the constructed directed graph meets the prediction requirement, and updating the directed graph under the condition that the constructed directed graph does not meet the prediction requirement. Positive examples may refer to behavior sequences that contain critical behaviors and negative examples may refer to behavior sequences that do not contain critical behaviors.

In the above embodiment of the present invention, the training set and the test set may be constructed as follows: determining a positive sample data set and a negative sample data set based on the plurality of behavior sequences; randomly dividing the positive sample data set to obtain a positive sample training set and a positive sample testing set; and randomly dividing the negative sample data set to obtain a negative sample training set and a negative sample testing set.

In an exemplary embodiment of the present invention, the behavior sequences may be classified according to whether the clean behavior sequence includes the target behavior, where the behavior sequence including the target behavior is classified into a positive sample data set, and vice versa into a negative sample training set. And further randomly selecting 70% of the positive sample data set as a positive sample training set, and the rest 30% of the positive sample data set as a positive sample testing set, and similarly, randomly selecting 70% of the negative sample data set as a negative sample training set, and the rest 30% of the negative sample data set as a negative sample testing set.

It should be noted that, the positive and negative sample training sets may be divided into a training set and a verification set based on random selection, where the training set is used to construct a directed graph, and the verification set is used to verify the effect of the directed graph in the training process, and is similar to the effect of the test set.

S208, constructing a first directed graph by using a training set;

in the above embodiment of the present invention, the first directed graph may be constructed as follows: constructing a second directed graph by using the positive sample training set; determining a first behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the positive sample edge shearing rate; determining a second behavior sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the negative sample edge shearing rate; and based on the first behavior sequence and the second behavior sequence, performing edge clipping on the second directed graph to obtain the first directed graph.

Optionally, the constructing the second directed graph by using the positive sample training set specifically includes the following steps: processing the positive sample training set, and determining nodes and edges in the second directed graph; performing characteristic extraction on the behavior data of the two behaviors connected with the edge to obtain characteristic data corresponding to the edge; and obtaining the weight value of the edge based on the characteristic data corresponding to the edge.

In an exemplary embodiment of the present invention, a user behavior is used as a node, and an inter-behavior transition is used as an edge, and an authorized directed graph is constructed, where an edge exists between two behavior nodes, which indicates that a user has executed between the two behaviors. Further, according to actual use requirements, the characteristics of the number of users of behavior pairs connected with the edges, the occurrence frequency of the behavior pairs, the number of users transferred to other nodes by the behavior nodes, the occurrence frequency of the behavior nodes and the like are extracted, and the weight value of each edge is calculated based on the characteristics.

In order to reduce the connectivity of the user behavior diagram and avoid the situation that the critical behavior can be reached no matter at which behavior point, the constructed behavior diagram needs to be trimmed, and edges which are not important in the positive sample and are important in the negative sample need to be trimmed.

The positive sample edge clipping rate in the above steps is used to characterize the ratio of the edge to be clipped from the positive sample in all positive samples, and the negative sample edge clipping rate is used to characterize the ratio of the edge to be clipped from the negative sample in all negative samples. It should be noted that, for the positive sample, an unimportant edge needs to be cut, that is, an edge with a smaller weight value needs to be cut; for negative samples, important edges need to be cut off, namely, edges with larger weight values need to be cut off.

In an exemplary embodiment of the present invention, a behavior sequence of the last several ranked bits in the positive samples may be selected based on the weight value of each edge, the first behavior sequence may be obtained, and a behavior sequence of the first several ranked bits in the negative samples may be selected, the second behavior sequence may be obtained, where the number of the first behavior sequence is determined by a product of the edge shearing rate of the positive samples and the behavior sequence in the positive samples, and the number of the second behavior sequence is determined by a product of the edge shearing rate of the positive samples and the edge shearing rate of the negative positive samples and the behavior sequence in the negative samples. After the first behavior sequence and the second behavior sequence which need to be cut off are determined, edges corresponding to the directed graph constructed by the positive sample training set can be deleted, and the purpose of cutting edges of the directed graph is achieved.

Step S209, testing the first directed graph based on the test set to obtain a test result;

the test result in the above steps is used to characterize whether the prediction result obtained after the Positive sample test set or the negative sample test set is input to the first directed graph is a Positive sample or a negative sample, wherein, for the Positive sample test set, if the prediction result is a Positive sample, the test result can be determined to be a True example (True Positive); for a negative sample test set, if the prediction result is a Positive sample, the test result can be determined to be a False Positive case (False Positive)

Step S210, under the condition that the test result does not meet the preset condition, updating the first directed graph until the test result meets the preset condition to obtain a directed graph;

the preset condition in the above step may be a stop condition for searching the directed graph for the optimal parameter, and in this embodiment, a True Positive example (True Positive) and a False Positive example (False Positive) are used as result evaluation criteria.

In the above embodiment of the present invention, whether the detection result satisfies the preset condition may be determined as follows: determining a first recognition rate based on a first test result of the positive sample test set, wherein the first recognition rate is used for representing the proportion of the positive sample test result in the first test result; determining a second recognition rate based on a second test result of the negative sample test set, wherein the second recognition rate is used for representing the proportion of the positive sample test result in the second test result; after the positive sample edge shearing rate and the negative sample edge shearing rate are updated, acquiring a first variable quantity of the first identification rate and a second variable quantity of the second identification rate; judging whether the first recognition rate is smaller than a preset value, whether the first recognition rate is smaller than a second recognition rate and whether the first variation is smaller than the second variation; if the first identification rate is smaller than a preset value, the first identification rate is smaller than the second identification rate, and the first variation is smaller than the second variation, determining that the test result meets a preset condition; and if the first identification rate is greater than or equal to a preset value, the first identification rate is greater than or equal to a second identification rate, or the first variation is greater than or equal to a second variation, determining that the test result does not meet the preset condition.

The first recognition rate may be an accuracy rate, the second recognition rate may be a false recognition rate, and the preset value may be an accuracy rate threshold value set in advance according to actual needs, and in this embodiment, 73% is taken as an example for description.

In an exemplary embodiment of the embodiments of the present invention, the preset condition comprises three stop conditions, the first stop condition being an accuracy of less than 73%; the second stopping condition is that the accuracy rate is reduced more than the error recognition rate after the parameters are updated, namely, the variation of the accuracy rate is larger than that of the error recognition rate after the parameters are updated; the third stopping condition is that the accuracy is lower than the misrecognition rate.

Optionally, the updating the first directed graph specifically includes the following steps: updating the positive sample edge shearing rate and the negative sample edge shearing rate based on a preset step length to obtain the updated positive sample edge shearing rate and the updated negative sample edge shearing rate; determining a third behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the updated positive sample edge shearing rate; determining a fourth line sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the updated negative sample edge shearing rate; and trimming the second directed graph based on the third behavior sequence and the fourth behavior sequence to obtain a third directed graph, wherein the third directed graph is determined to be the directed graph under the condition that the test result corresponding to the second directed graph meets the preset condition.

The preset step length in the above steps may be set according to actual needs, and the present invention is not particularly limited to this.

In an exemplary embodiment of the present invention, the constructed directed graph may be tested based on a test set, and if it is determined that the test result does not meet the three stop conditions, that is, does not meet any one of the stop conditions, the parameters are updated according to a preset step length, and the edges of the constructed original directed graph (i.e., the second directed graph) are trimmed until the three stop conditions are met, and a final directed graph (i.e., the third directed graph) is output for prediction.

By trimming the directed graph, the false recognition rate can be reduced to the maximum extent while a certain prediction accuracy rate is ensured, so that the directed graph with the best prediction effect is obtained.

Step S211, retrieving the directed graph, and determining at least one behavior path for transferring the non-target behavior to the target behavior, and a weight value of each behavior path.

In the embodiment of the application, the authorized directed graph is constructed by using the historical behavior data of the whole user group, so that the technical effects of improving the prediction accuracy, improving the user experience and improving the user stickiness are achieved, and the technical problems that the accuracy of user behavior prediction is not high and the user experience is poor in the related technology are solved.

Example 3

As shown in fig. 3, the method comprises the steps of:

step S301, acquiring behavior data of a plurality of objects;

step S302, constructing a directed graph based on the behavior data of a plurality of objects, wherein the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge;

step S303, retrieving the directed graph, and determining at least one behavior path for transferring the non-target behavior to the target behavior and the weight value of each behavior path, wherein the number of different behaviors contained in the behavior path is less than or equal to the preset number;

step S304, acquiring a target behavior sequence of a target object;

the target object in the above steps may be a user currently using APP, or a key user that an operator desires to pay attention to, and the target behavior sequence may be a series of operation behaviors currently performed by the user, where the sequence includes multiple behaviors performed by the user and a transition relationship between different behaviors, and therefore, may be used as a behavior path.

Step S305, matching the target behavior data of the target behavior sequence with preset behavior data;

step S306, if the target behavior data is successfully matched with the preset behavior data, the target behavior data is removed to obtain a cleaned target behavior sequence;

in an exemplary embodiment of the present invention, similar to the directed graph construction process, after the target behavior sequence is obtained, the obtained target behavior sequence may be cleaned based on preset behavior data, so as to obtain a clean behavior sequence.

Step S307, matching the cleaned target behavior sequence with at least one behavior path, and determining a target behavior path;

the target behavior path in the above steps may be a behavior path predicted based on the target behavior sequence that the user is most likely to reach the target behavior.

In an exemplary embodiment of the present invention, in order to implement prediction of a user behavior, a last behavior in a target behavior sequence may be matched with all behavior paths, whether the last behavior exists in the behavior paths is determined, and if the last behavior exists in a certain behavior path, it is determined that the target behavior sequence is successfully matched with the behavior path, so as to determine that the behavior path is the target behavior path; and if the target behavior sequence does not exist in a certain behavior path, determining that the target behavior sequence fails to be matched with the behavior path.

It should be noted that there is often more than one behavior path that is successfully matched with the target behavior sequence, and further, one or more behavior paths with extremely large weight values may be selected as the target behavior path based on the weight values of the behavior paths.

Step S308, acquiring a weight value of the target behavior path;

step S309, determining whether target information is pushed or not based on the weight value of the target behavior path, wherein the target information is used for prompting the target object to execute the target behavior;

the target information in the above steps may be an intervention suggestion for intervening the user behavior, and the user may execute the target behavior in the subsequent APP operation process by intervening the user needing to intervene.

In an exemplary embodiment of the present invention, for a behavior path with a lower weight value, the probability that a user contacts a target behavior is smaller, and therefore, intervention is not suggested, and it is determined that target information is not pushed; (ii) a For the action path with higher weight value, the user has a greater possibility of autonomously executing the target action in the actual use process, so intervention is not suggested, and target information is not pushed; in the action paths other than the two cases, the user has an opportunity to contact the target action, and in order to ensure that the user can contact the target action, the action of the user can be interfered, so that the target information is determined to be pushed.

Optionally, in order to determine whether to push target information based on the weight value of the target behavior path accurately, at least one behavior path is searched from the constructed directed graph, and after the corresponding weight value is calculated, the behavior paths may be sorted in a descending order according to the weight value, and divided into behavior paths with high, medium, and low level probabilities according to a ratio of 3:5: 2. After the weighted value of the target behavior path is obtained, the corresponding level probability can be determined, if the level probability is a middle level probability, the intervention is determined to be needed, and target information can be pushed; if the probability is high or low, intervention is not suggested, and target information can not be pushed.

It should be noted that the division criteria of the three level probabilities may be set according to actual needs, and in the embodiment of the present invention, a ratio of 3:5:2 is taken as an example for description.

Step S310, after the target information is determined to be pushed, whether the target object executes the target behavior is monitored;

in an exemplary embodiment of the present invention, after the user behavior is interfered, whether the user contacts the target behavior after being interfered needs to be monitored, and the directed graph is updated according to the monitoring result, so that the accuracy of the user behavior prediction is further improved.

It should be noted that, if the user contacts the target behavior, that is, the obtained target behavior sequence includes the target behavior, the target behavior sequence of the user at this time may be used as a test set, and the directed graph is updated.

In step S311, when the target object does not execute the target behavior, the directed graph is updated based on the target behavior sequence.

In an exemplary embodiment of the present invention, after the user behavior is interfered, whether the user contacts the target behavior after being interfered needs to be monitored, and if the user does not contact the target behavior, that is, the obtained target behavior sequence does not include the target behavior, a prediction error sequence is collected, and the directed graph is periodically analyzed and updated.

In the embodiment of the application, the authorized directed graph is constructed by using the historical behavior data of the whole user group, and the future behavior path of the user is predicted by the authorized directed graph, so that the technical effects of improving the prediction accuracy, improving the user experience and improving the user stickiness are achieved, and the technical problems that the accuracy of user behavior prediction is not high and the user experience is poor in the related technology are solved.

Example 4

The embodiment of the present application further provides a method for processing a user behavior, where the method is applied to an electronic device, such as a computer terminal, a smart phone, a tablet computer, and the like, but is not limited thereto, and the method is not limited in this application.

Fig. 4 is a flowchart of a processing method of a fourth user behavior according to an embodiment of the present application, and as shown in fig. 4, the method includes the following steps:

step S402, acquiring a target behavior sequence of a target object;

Step S404, matching the target behavior sequence with at least one behavior path to determine a target behavior path, wherein the at least one behavior path is a behavior path for transferring a non-target behavior determined by retrieving a directed graph to the target behavior, the number of different behaviors contained in the behavior path is less than or equal to a preset number, the directed graph is constructed based on behavior data of a plurality of objects, and the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge;

Step S406, acquiring a weight value of the target behavior path, wherein the weight value is determined by retrieving the directed graph;

Step S408, determining whether to push target information based on the weight value of the target behavior path, where the target information is used to prompt the target object to execute the target behavior.

In the embodiment of the application, a directed graph is constructed based on behavior data of a plurality of objects, the directed graph is retrieved, at least one behavior path for transferring a non-target behavior to a target behavior is determined, and further, the target behavior path can be determined by matching a target behavior sequence of a target object with the at least one behavior path, so that the purpose of constructing a entitled directed graph by using historical behavior data of the whole user group and predicting a future behavior path of a user through the entitled directed graph is achieved, the technical effects of improving prediction accuracy, improving user experience and improving user stickiness are achieved, and the technical problems that in the related technology, the accuracy of user behavior prediction is not high and the user experience is poor are solved.

Example 5

The user behavior processing method provided by the embodiment of the invention can be applied to the APP of the electronic equipment. In the embodiment of the present invention, the present invention is described with reference to fig. 5 to 7 by taking a class optimization master APP as an example.

In the class optimization master APP, operation intervention can be performed on a specific user generating a certain real-time behavior sequence according to the actual business process of the class optimization master, and the specific user is guided to contact a key function. For example, the key function may be a broadcasting station function, which is used for providing a teacher user with a notification, a job issuing function or a card punching task issuing function to a bound class, and a parent user in the class may view corresponding content in the APP, so as to facilitate communication between the parent and the teacher. However, due to the habit problem of users, the percentage of users using the broadcasting station function is low, and the operator wants to promote the function, so that certain intervention measures can be performed on part of users at a certain time point to guide the users to contact the function.

The processing method of the user behavior provided by the embodiment of the invention can be divided into an engine terminal and a service server terminal, wherein the engine terminal utilizes historical one-week behavior data of the user to construct a weighted directed graph, and calculates a path from a non-key behavior to a key behavior in the directed graph within a certain step number and corresponding weight; and the server end acquires the real-time behavior sequence of the user, returns whether the user is a key user needing intervention or not, and gives the probability of contacting the key function and the predicted contact path of the user.

Fig. 5 shows a specific implementation flow of the engine end, and as shown in fig. 5, a batch of key behaviors, namely functions which are currently used in the app in a small amount but are desired to be contacted by the operator, need to be predefined first; the method comprises the steps that an engine terminal obtains historical behavior data of all users in an app; after data cleaning is carried out on some predefined noise behaviors, stain behaviors and the like, behavior sequence cutting is carried out on the predefined noise behaviors, stain behaviors and the like, and behavior sequence cleaning is carried out; classifying the sequence into a positive sample and a negative sample according to whether the sequence contains a key behavior, namely, the sequence contains the key behavior and is regarded as the positive sample, otherwise, the sequence is regarded as the negative sample, and filtering the sequence under certain conditions after classification; simultaneously, carrying out training set and test set segmentation to construct a positive training set and a negative training set and a test set; constructing a user behavior diagram by using the positive sample training set, constructing characteristics, calculating the weight of each edge, and then performing edge trimming on the network by using the negative sample training set under certain conditions to obtain an optimal user behavior diagram; and carrying out conditional path search on all non-critical behaviors to critical behaviors in the graph according to the behavior diagram, and giving a weight corresponding to the path.

Fig. 6 shows a specific implementation flow of the engine end construction data set, in which positive and negative samples are constructed first, then samples are randomly selected to construct a training set and a test set, and finally samples of the training set are further randomly selected to construct the training set and a verification set.

Fig. 7 shows a specific implementation flow of the server, as shown in fig. 7, the server obtains a real-time behavior sequence of the user, searches whether a last behavior in the behavior sequence that the user satisfies a condition at a certain time point exists in a given path, determines whether a corresponding weight satisfies an intervention condition if the last behavior exists, and returns a behavior path and a weight that the user is most likely to reach a key function and a corresponding high, medium, and low level weight if the last behavior satisfies the intervention condition.

By the constructed directed graph of the user behaviors, whether the user is a user needing intervention or not can be judged according to the real-time behavior sequence of the user, the future behavior path of the user is predicted, the appropriate user is guided to use the key function through the appropriate path, and the user experience is improved and the user viscosity is improved. Meanwhile, more data are used by the user on the APP, and the user data analysis and mining in the later stage are facilitated, so that better service and virtuous circle are provided for the user.

Example 6

The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.

As shown in fig. 8, the processing means of the user behavior may be implemented by software, hardware or a combination of both as all or a part of the electronic device. The apparatus includes a first acquisition module 82, a construction module 84, and a retrieval module 86.

The first obtaining module 82 is used for obtaining behavior data of a plurality of objects;

the building module 84 is configured to build a directed graph based on the behavior data of the plurality of objects, where the directed graph includes: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge;

the retrieval module 86 is configured to retrieve the directed graph, determine at least one behavior path where the non-target behavior is transferred to the target behavior, and determine a weight value of each behavior path, where a number of different behaviors included in the behavior path is less than or equal to a preset number;

on the basis of the above embodiment, the building module includes: the device comprises a segmentation unit, a storage unit and a processing unit, wherein the segmentation unit is used for segmenting behavior data of a plurality of objects to obtain a plurality of behavior sequences; a first construction unit, configured to construct a training set and a test set based on a plurality of behavior sequences, where the training set includes: a positive sample training set and a negative sample training set, the test set comprising: the method comprises the steps of a positive sample test set and a negative sample test set, wherein the positive sample is a behavior sequence containing a target behavior, and the negative sample is a behavior sequence not containing the target behavior; the second construction unit is used for constructing the first directed graph by utilizing the training set; the test unit is used for testing the first directed graph based on the test set to obtain a test result; and the updating unit is used for updating the first directed graph under the condition that the test result does not meet the preset condition until the test result meets the preset condition to obtain the directed graph.

On the basis of the above embodiment, the second building unit includes: the construction subunit is used for constructing a second directed graph by utilizing the positive sample training set; the first determining subunit is configured to determine a first behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the positive sample edge shearing rate; the second determining subunit is configured to determine a second behavior sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the negative sample edge shearing rate; and the edge clipping subunit is used for clipping the edge of the second directed graph based on the first behavior sequence and the second behavior sequence to obtain the first directed graph.

On the basis of the foregoing embodiment, the updating unit includes: the first updating subunit is used for updating the positive sample edge shearing rate and the negative sample edge shearing rate based on a preset step length to obtain the updated positive sample edge shearing rate and the updated negative sample edge shearing rate; a third determining subunit, configured to determine a third behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the updated positive sample edge shearing rate; a fourth determining subunit, configured to determine a fourth row of the negative sample training set as a sequence based on the weight value of each edge in the second directed graph and the updated negative sample edge shearing rate; the edge clipping subunit is further configured to clip an edge of the second directed graph based on the third row sequence and the fourth row sequence to obtain a third directed graph, where the third directed graph is determined to be the directed graph when a test result corresponding to the second directed graph meets a preset condition.

On the basis of the above embodiment, the building subunit includes: the first processing submodule is used for processing the positive sample training set and determining nodes and edges in the second directed graph; the extraction submodule is used for extracting the characteristics of the behavior data of the two behaviors connected with the edge to obtain the characteristic data corresponding to the edge; and the second processing submodule is used for obtaining the weight value of the edge based on the characteristic data corresponding to the edge.

On the basis of the above embodiment, the first building unit includes: a fifth determining subunit, configured to determine a positive sample data set and a negative sample data set based on the plurality of behavior sequences; the first dividing unit is used for randomly dividing the positive sample data set to obtain a positive sample training set and a positive sample testing set; and the second dividing subunit is used for randomly dividing the negative sample data set to obtain a negative sample training set and a negative sample testing set.

On the basis of the above embodiment, the apparatus further includes: the judging module is used for judging whether the test result meets the preset condition or not, wherein the judging module comprises: the first determining unit is used for determining a first recognition rate based on a first test result of the positive sample test set, wherein the first recognition rate is used for representing the proportion of the positive sample test result in the first test result; the second determining unit is used for determining a second recognition rate based on a second test result of the negative sample test set, wherein the second recognition rate is used for representing the proportion of the positive sample test result in the second test result; the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a first variation of a first identification rate and a second variation of a second identification rate after updating the positive sample edge shearing rate and the negative sample edge shearing rate; the first judging unit is used for judging whether the first recognition rate is smaller than a preset value, whether the first recognition rate is smaller than a second recognition rate and whether the first variation is smaller than the second variation; the third determining unit is used for determining that the test result meets the preset condition if the first identification rate is smaller than the preset value, the first identification rate is smaller than the second identification rate, and the first variation is smaller than the second variation; and the fourth determining unit is used for determining that the test result does not meet the preset condition if the first identification rate is greater than or equal to the preset value, the first identification rate is greater than or equal to the second identification rate, or the first variation is greater than or equal to the second variation.

On the basis of the above embodiment, the building module further includes: the matching unit is used for matching the behavior data with preset behavior data; the first removing unit is used for removing the behavior data to obtain cleaned behavior data if the behavior data is successfully matched with the preset behavior data; the dividing unit is further used for dividing the cleaned behavior data to obtain a plurality of behavior sequences.

On the basis of the above embodiment, the building module further includes: the second judging unit is used for judging whether the two adjacent behavior sequences are the same or not; the second removing unit is used for removing any one behavior sequence in the two adjacent behavior sequences to obtain a cleaned behavior sequence if the two adjacent behavior sequences are the same; the first construction unit is also used for constructing a training set and a testing set based on the cleaned behavior sequence.

On the basis of the above embodiment, the apparatus further includes: the second acquisition module is used for acquiring a target behavior sequence of the target object; the matching module is used for matching the target behavior sequence with at least one behavior path to determine a target behavior path; the third obtaining module is used for obtaining the weight value of the target behavior path; the determining module is used for determining whether target information is pushed or not based on the weight value of the target behavior path, wherein the target information is used for prompting the target object to execute the target behavior.

On the basis of the above embodiment, the apparatus further includes: the monitoring module is used for monitoring whether the target object executes the target behavior; and the updating module is used for updating the directed graph based on the target behavior sequence under the condition that the target object does not execute the target behavior.

Optionally, the apparatus further comprises: the matching module is also used for matching the target behavior data of the target behavior sequence with preset behavior data; the removing module is used for removing the target behavior data to obtain a cleaned target behavior sequence if the target behavior data is successfully matched with the preset behavior data; the matching module is further used for matching the cleaned target behavior sequence with at least one behavior path to determine a target behavior path.

It should be noted that, when the processing apparatus for user behavior provided in the foregoing embodiment executes the processing method for user behavior, only the division of the functional modules is taken as an example, and in practical applications, the functions may be distributed to different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules, so as to complete all or part of the functions described above. In addition, the processing apparatus for user behavior and the processing method for user behavior provided by the foregoing embodiments belong to the same concept, and the detailed implementation process is described in the method embodiments, which is not described herein again.

Example 7

As shown in fig. 9, the processing means of the user behavior may be implemented by software, hardware or a combination of both as all or a part of the electronic device. The apparatus includes a second obtaining module 92, a matching module 94, a third obtaining module 96, and a determining module 98.

The second obtaining module 92 is configured to obtain a target behavior sequence of a target object;

the matching module 94 is configured to match the target behavior sequence with at least one behavior path and determine a target behavior path, where the at least one behavior path is a behavior path that is transferred from a non-target behavior determined by retrieving a directed graph to a target behavior, a number of different behaviors included in the behavior path is less than or equal to a preset number, the directed graph is constructed based on behavior data of a plurality of objects, and the directed graph includes: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge;

the third obtaining module 96 is configured to obtain a weight value of the target behavior path, where the weight value is determined by retrieving a directed graph;

the determining module 98 is configured to determine whether to push target information based on the weight value of the target behavior path, where the target information is used to prompt the target object to execute the target behavior.

Example 8

An embodiment of the present application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executing the method steps in the embodiments shown in fig. 1 to 7, and a specific execution process may refer to specific descriptions of the embodiments shown in fig. 1 to 7, which are not described herein again.

The device in which the storage medium is located may be an electronic device.

Example 9

As shown in fig. 10, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.

Wherein a communication bus 1002 is used to enable connective communication between these components.

The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.

The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.

Processor 1001 may include one or more processing cores, among other things. The processor 1001, which is connected to various parts throughout the electronic device 1000 using various interfaces and lines, performs various functions of the electronic device 1000 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1005 and calling data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1001 may integrate one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 1001, but may be implemented by a single chip.

The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 10, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and an operating application of the electronic device.

In the electronic device 1000 shown in fig. 10, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; and the processor 1001 may be configured to invoke an operating application of the electronic device stored in the memory 1005, and specifically perform the following operations:

acquiring behavior data of a plurality of objects; constructing a directed graph based on the behavior data of the plurality of objects, wherein the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing the transfer relationship between the two behaviors connected through the edge; searching the directed graph, and determining at least one behavior path for transferring the non-target behavior to the target behavior and the weight value of each behavior path, wherein the number of different behaviors contained in the behavior paths is less than or equal to the preset number; acquiring a target behavior sequence of a target object; and matching the target behavior sequence with at least one behavior path to determine a target behavior path.

In one embodiment, the operating system of the electronic device is an android system, and in the android system, the processor 1001 further performs the following steps:

segmenting the behavior data of a plurality of objects to obtain a plurality of behavior sequences; constructing a training set and a testing set based on a plurality of behavior sequences, wherein the training set comprises: a positive sample training set and a negative sample training set, the test set comprising: the method comprises the steps of a positive sample test set and a negative sample test set, wherein the positive sample is a behavior sequence containing a target behavior, and the negative sample is a behavior sequence not containing the target behavior; constructing a first directed graph by utilizing a training set; testing the first directed graph based on the test set to obtain a test result; and under the condition that the test result does not meet the preset condition, updating the first directed graph until the test result meets the preset condition to obtain the directed graph.

In one embodiment, the processor 1001 further performs the steps of:

determining a first behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the positive sample edge shearing rate; determining a second behavior sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the negative sample edge shearing rate; and based on the first behavior sequence and the second behavior sequence, performing edge clipping on the second directed graph to obtain the first directed graph.

In one embodiment, the processor 1001 further performs the steps of:

updating the positive sample edge shearing rate and the negative sample edge shearing rate based on a preset step length to obtain the updated positive sample edge shearing rate and the updated negative sample edge shearing rate; determining a third behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the updated positive sample edge shearing rate; determining a fourth line sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the updated negative sample edge shearing rate; and trimming the second directed graph based on the third behavior sequence and the fourth behavior sequence to obtain a third directed graph, wherein the third directed graph is determined to be the directed graph under the condition that the test result corresponding to the second directed graph meets the preset condition.

In one embodiment, the processor 1001 further performs the steps of:

processing the positive sample training set, and determining nodes and edges in the second directed graph; performing characteristic extraction on the behavior data of the two behaviors connected with the edge to obtain characteristic data corresponding to the edge; and obtaining the weight value of the edge based on the characteristic data corresponding to the edge.

In one embodiment, the processor 1001 further performs the steps of:

determining a positive sample data set and a negative sample data set based on the plurality of behavior sequences; randomly dividing the positive sample data set to obtain a positive sample training set and a positive sample testing set; and randomly dividing the negative sample data set to obtain a negative sample training set and a negative sample testing set.

In one embodiment, the processor 1001 further performs the steps of:

determining a first recognition rate based on a first test result of the positive sample test set, wherein the first recognition rate is used for representing the proportion of the positive sample test result in the first test result; determining a second recognition rate based on a second test result of the negative sample test set, wherein the second recognition rate is used for representing the proportion of the positive sample test result in the second test result; after the positive sample edge shearing rate and the negative sample edge shearing rate are updated, acquiring a first variable quantity of the first identification rate and a second variable quantity of the second identification rate; judging whether the first recognition rate is smaller than a preset value, whether the first recognition rate is smaller than a second recognition rate and whether the first variation is smaller than the second variation; if the first identification rate is smaller than a preset value, the first identification rate is smaller than the second identification rate, and the first variation is smaller than the second variation, determining that the test result meets a preset condition; and if the first identification rate is greater than or equal to a preset value, the first identification rate is greater than or equal to a second identification rate, or the first variation is greater than or equal to a second variation, determining that the test result does not meet the preset condition.

In one embodiment, the processor 1001 further performs the steps of:

before the behavior data of a plurality of objects are segmented to obtain a plurality of behavior sequences, matching the behavior data with preset behavior data; if the behavior data is successfully matched with the preset behavior data, the behavior data is removed to obtain cleaned behavior data; and segmenting the cleaned behavior data to obtain a plurality of behavior sequences.

In one embodiment, the processor 1001 further performs the steps of:

before a training set and a test set are constructed based on a plurality of behavior sequences, judging whether two adjacent behavior sequences are the same; if the two adjacent behavior sequences are the same, removing any one behavior sequence from the two adjacent behavior sequences to obtain a cleaned behavior sequence; and constructing a training set and a testing set based on the cleaned behavior sequence.

In one embodiment, the processor 1001 further performs the steps of:

matching the target behavior sequence with at least one behavior path, and acquiring a weight value of the target behavior path after determining the target behavior path; and determining whether to push target information based on the weight value of the target behavior path, wherein the target information is used for prompting the target object to execute the target behavior.

In one embodiment, the processor 1001 further performs the steps of:

after determining to push the target information, monitoring whether the target object executes the target behavior; and updating the directed graph based on the target behavior sequence under the condition that the target object does not execute the target behavior.

In one embodiment, the processor 1001 further performs the steps of:

matching target behavior data of the target behavior sequence with preset behavior data before matching the target behavior sequence with at least one behavior path and determining the target behavior path; if the target behavior data is successfully matched with the preset behavior data, the target behavior data is removed, and a cleaned target behavior sequence is obtained; and matching the cleaned target behavior sequence with at least one behavior path to determine a target behavior path.

The method has the advantages that the authorized directed graph is constructed by utilizing the historical behavior data of the whole user group, and the future behavior path of the user is predicted through the authorized directed graph, so that the technical effects of improving the prediction accuracy, improving the user experience and improving the user stickiness are achieved, and the technical problems that the accuracy of user behavior prediction is low and the user experience is poor in the related technology are solved.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for processing user behaviors is characterized by comprising the following steps:

acquiring behavior data of a plurality of objects;

constructing a directed graph based on the behavior data of the plurality of objects, wherein the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing a transfer relationship between two behaviors connected through the edge;

and retrieving the directed graph, and determining at least one behavior path for transferring the non-target behavior to the target behavior and the weight value of each behavior path, wherein the number of different behaviors contained in the behavior paths is less than or equal to the preset number.

2. The method of claim 1, wherein constructing a directed graph based on the behavioral data of the plurality of objects comprises:

segmenting the behavior data of the plurality of objects to obtain a plurality of behavior sequences;

constructing a training set and a test set based on the plurality of behavior sequences, wherein the training set comprises: a positive sample training set and a negative sample training set, the test set comprising: a positive sample test set and a negative sample test set, wherein the positive sample is a behavior sequence containing the target behavior, and the negative sample is a behavior sequence not containing the target behavior;

constructing a first directed graph by utilizing the training set;

testing the first directed graph based on the test set to obtain a test result;

and under the condition that the test result does not meet the preset condition, updating the first directed graph until the test result meets the preset condition to obtain the directed graph.

3. The method of claim 2, wherein constructing an initial directed graph using the training set comprises:

constructing a second directed graph by using the positive sample training set;

determining a first behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the positive sample edge shearing rate;

determining a second behavior sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the negative sample edge shearing rate;

and based on the first behavior sequence and the second behavior sequence, performing edge clipping on the second directed graph to obtain the first directed graph.

4. The method of claim 3, wherein updating the first directed graph comprises:

updating the positive sample edge shearing rate and the negative sample edge shearing rate based on a preset step length to obtain the updated positive sample edge shearing rate and the updated negative sample edge shearing rate;

determining a third behavior sequence in the positive sample training set based on the weight value of each edge in the second directed graph and the updated positive sample edge shearing rate;

determining a fourth line sequence in the negative sample training set based on the weight value of each edge in the second directed graph and the updated negative sample edge shearing rate;

and trimming the second directed graph based on the third behavior sequence and the fourth behavior sequence to obtain a third directed graph, wherein the third directed graph is determined to be the directed graph under the condition that a test result corresponding to the second directed graph meets the preset condition.

5. The method of claim 3, wherein constructing a second directed graph using the training set of positive samples comprises:

processing the positive sample training set to determine nodes and edges in the second directed graph;

performing feature extraction on the behavior data of the two behaviors connected with the edge to obtain feature data corresponding to the edge;

and obtaining the weight value of the edge based on the characteristic data corresponding to the edge.

6. The method of claim 2, wherein constructing a training set and a test set based on the plurality of behavior sequences comprises:

determining a positive sample data set and a negative sample data set based on the plurality of behavior sequences;

randomly dividing the positive sample data set to obtain the positive sample training set and the positive sample testing set;

and randomly dividing the negative sample data set to obtain the negative sample training set and the negative sample testing set.

7. The method of claim 2, wherein determining whether the test result satisfies the predetermined condition comprises:

determining a first recognition rate based on a first test result of the positive sample test set, wherein the first recognition rate is used for representing the proportion of the positive sample test result in the first test result;

determining a second recognition rate based on a second test result of the negative sample test set, wherein the second recognition rate is used for representing the proportion of the positive sample test result in the second test result;

after the positive sample edge shearing rate and the negative sample edge shearing rate are updated, acquiring a first variation of the first identification rate and a second variation of the second identification rate;

judging whether the first recognition rate is smaller than a preset value, whether the first recognition rate is smaller than the second recognition rate, and whether the first variation is smaller than the second variation;

if the first identification rate is smaller than the preset value, the first identification rate is smaller than the second identification rate, and the first variation is smaller than the second variation, determining that the test result meets the preset condition;

and if the first identification rate is greater than or equal to the preset value, the first identification rate is greater than or equal to the second identification rate, or the first variation is greater than or equal to the second variation, determining that the test result does not meet the preset condition.

8. The method of claim 2, wherein prior to segmenting the behavior data of the plurality of objects into a plurality of behavior sequences, the method further comprises:

matching the behavior data with preset behavior data;

if the behavior data is successfully matched with the preset behavior data, the behavior data is removed to obtain cleaned behavior data;

and segmenting the cleaned behavior data to obtain the plurality of behavior sequences.

9. The method of claim 2, wherein prior to constructing a training set and a test set based on the plurality of behavior sequences, the method further comprises:

judging whether two adjacent behavior sequences are the same;

if the two adjacent behavior sequences are the same, removing any one behavior sequence from the two adjacent behavior sequences to obtain a cleaned behavior sequence;

and constructing the training set and the testing set based on the cleaned behavior sequence.

10. The method of any one of claims 1 to 9, wherein after retrieving the directed graph, determining at least one behavior path for a transition of a non-target behavior to a target behavior, and a weight value for each behavior path, the method further comprises:

acquiring a target behavior sequence of a target object;

matching the target behavior sequence with the at least one behavior path to determine a target behavior path;

acquiring a weight value of the target behavior path;

and determining whether to push target information based on the weight value of the target behavior path, wherein the target information is used for prompting the target object to execute the target behavior.

11. The method of claim 10, wherein after determining to push the target information, the method further comprises:

monitoring whether the target object executes the target behavior;

updating the directed graph based on the target behavior sequence when the target object does not execute the target behavior.

12. The method of claim 10, wherein prior to matching the target behavior sequence with the at least one behavior path to determine a target behavior path, the method further comprises:

matching the target behavior data of the target behavior sequence with preset behavior data;

if the target behavior data is successfully matched with the preset behavior data, the target behavior data is removed, and a cleaned target behavior sequence is obtained;

and matching the cleaned target behavior sequence with the at least one behavior path to determine the target behavior path.

13. A method for processing user behaviors is characterized by comprising the following steps:

acquiring a target behavior sequence of a target object;

matching the target behavior sequence with at least one behavior path to determine a target behavior path, wherein the at least one behavior path is a behavior path for transferring a non-target behavior determined by retrieving the directed graph to a target behavior, the number of different behaviors contained in the behavior path is less than or equal to a preset number, the directed graph is constructed based on behavior data of a plurality of objects, and the directed graph comprises: the node is used for representing different behaviors in the behavior data, and the edge is used for representing a transfer relationship between two behaviors connected through the edge;

obtaining a weight value of the target behavior path, wherein the weight value is determined by retrieving the directed graph;

14. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to carry out the method steps according to any one of claims 1 to 13.

15. An electronic device, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 13.