Method and device for pushing information

Abstract

The embodiments of the present disclosure disclose a method and apparatus for pushing information. A specific implementation of the method includes: obtaining behavioral data of users in a user group, and determining a classification identifier set for the user group, wherein the classification identifier in the classification identifier set is used to indicate the classification method for classifying the user group to obtain sub-user clusters; for the classification identifier in the classification identifier set, determining the difference between the behavioral data of the sub-user groups in the sub-user cluster corresponding to the classification identifier based on the behavioral data; selecting a classification identifier from the classification identifier set as a target classification identifier based on the corresponding difference; and pushing information to each sub-user group in the sub-user cluster corresponding to the target classification identifier. This implementation realizes personalized information push.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and more particularly, to a method and apparatus for pushing information. Background Art

[0002] With the rapid development of network technology, people's lives are gradually shifting to the internet and mobile internet. While the internet provides convenience, it also brings the problem of information explosion. For users, how to find the information they need amidst the vast amount of information on the internet is a problem that needs to be solved or further optimized.

[0003] Based on this, some researchers have proposed using methods such as data mining and data analysis based on big data to help users filter desired information from massive amounts of information. Currently, many applications such as news, social networking, e-commerce platforms, and reading apps use this method to push or display selected information to users, further enhancing the user experience when using the internet.

[0004] For example, some applications or platforms often assign corresponding user tags to each user based on the mining and analysis of user historical behavior data. In turn, based on user tags, users can be divided into user groups corresponding to different tags, making it easier to provide different services (such as pushing different information, etc.) to user groups with different tags, so that users can quickly obtain the services they need. Summary of the Invention

[0005] Embodiments of the present disclosure provide a method and apparatus for pushing information.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for pushing information, the method comprising: obtaining behavioral data of users in a user group, and determining a classification identifier set for the user group, wherein the classification identifier in the classification identifier set is used to indicate a classification method for classifying the user group to obtain sub-user clusters; for the classification identifier in the classification identifier set, determining the difference between the behavioral data of the sub-user groups in the sub-user cluster corresponding to the classification identifier based on the behavioral data; selecting a classification identifier from the classification identifier set as a target classification identifier based on the corresponding difference; and pushing information to each sub-user group in the sub-user cluster corresponding to the target classification identifier.

[0007] In a second aspect, an embodiment of the present disclosure provides a device for pushing information, the device comprising: an acquisition unit configured to acquire behavioral data of users in a user group, and determine a classification identifier set for the user group, wherein the classification identifier in the classification identifier set is used to indicate a classification method for classifying the user group to obtain sub-user clusters; a determination unit configured to determine, for a classification identifier in the classification identifier set, based on the behavioral data of users in the user group, a degree of difference between behavioral data of sub-user groups in the sub-user cluster corresponding to the classification identifier; a selection unit configured to select a classification identifier from the classification identifier set as a target classification identifier based on the corresponding degree of difference; and a push unit configured to push information to each sub-user group in the sub-user cluster corresponding to the target classification identifier.

[0008] In a third aspect, an embodiment of the present disclosure provides a server comprising: one or more processors; a storage device for storing one or more programs; and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0009] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0010] The method and device for pushing information provided by the embodiments of the present disclosure determine the difference between the behavior data of users in each sub-user group obtained under each classification method of the user group through the behavior data of each user in the user group, so that the distinction between the sub-user groups obtained under each classification method can be understood, and then the classification method can be selected according to the difference corresponding to each classification method, and information can be pushed to each sub-user group corresponding to this classification method. In this way, flexible control of the classification method of the user group can be achieved according to different needs, and flexible control of the information pushed to each user in the user group can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objects and advantages of the present disclosure will become more apparent from a reading of the detailed description of non-limiting embodiments made with reference to the following drawings:

[0012] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;

[0013] Figure 2 is a flow chart of an embodiment of a method for pushing information according to the present disclosure;

[0014] Figure 3is a schematic diagram of an application scenario of a method for pushing information according to an embodiment of the present disclosure;

[0015] Figure 4 is a process for updating a target classification identifier according to an embodiment of the present disclosure;

[0016] Figure 5 is a structural diagram of an embodiment of a device for pushing information according to the present disclosure;

[0017] Figure 6 It is a schematic structural diagram of an electronic device suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] The present disclosure will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0019] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] Figure 1 An exemplary architecture 100 is shown to which an embodiment of a method for pushing information or an apparatus for pushing information disclosed herein may be applied.

[0021] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0022] Terminal devices 101, 102, and 103 interact with server 105 via network 104 to receive or send messages, etc. Various client applications may be installed on terminal devices 101, 102, and 103, such as browser applications, search applications, shopping applications, social platforms, information flow applications, and the like.

[0023] The terminal devices 101, 102, and 103 can be hardware or software. When the terminal devices 101, 102, and 103 are hardware, they can be various electronic devices, including but not limited to smartphones, tablet computers, e-book readers, laptop computers, and desktop computers. When the terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules (for example, multiple software or software modules for providing distributed services), or they can be implemented as a single software or software module. No specific limitation is made here.

[0024] The server 105 may be a server that provides various services, such as a backend server that supports client applications installed on the terminal devices 101, 102, and 103. For example, the server 105 may obtain behavioral data of users in a user group from the terminal devices 101, 102, and 103, and determine, based on the behavioral data, the differences corresponding to different classification methods for the user group. Then, based on the obtained differences, the server 105 selects a classification method for the user group and pushes information to the sub-user groups obtained using the selected classification method.

[0025] It should be noted that the behavior data of users in the above-mentioned user group can also be directly stored locally on the server 105. The server 105 can directly extract the behavior data of users in the locally stored user group and process it. At this time, the terminal devices 101, 102, 103 and the network 104 may not exist.

[0026] It should be noted that the method for pushing information provided in the embodiments of the present disclosure is generally executed by the server 105 , and accordingly, the device for pushing information is generally provided in the server 105 .

[0027] It should also be noted that the terminal devices 101, 102, and 103 can also obtain the behavioral data of users in the user group from a local or other storage device, and based on this behavioral data, determine the difference corresponding to different classification methods of the user group. Then, based on the obtained difference, select a classification method for the user group and push information to the sub-user groups obtained using the selected classification method. In this case, the method for pushing information can also be executed by the terminal devices 101, 102, and 103, and accordingly, the device for pushing information can also be set in the terminal devices 101, 102, and 103. In this case, the exemplary system architecture 100 can be free of the server 105 and the network 104.

[0028] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server 105 is software, it can be implemented as multiple software or software modules (for example, multiple software or software modules for providing distributed services), or it can be implemented as a single software or software module. No specific limitations are given here.

[0029] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0030] Continue to refer Figure 2 , which shows a process 200 of an embodiment of a method for pushing information according to the present disclosure. The method for pushing information includes the following steps:

[0031] Step 201: Obtain behavioral data of users in a user group, and determine a classification identifier set for the user group.

[0032] In this embodiment, user groups are composed of users. User groups can be determined based on actual application requirements. For example, a technical staff member can pre-specify user groups. In another example, user groups can be determined based on screening criteria. The screening criteria can be flexibly set. For example, different screening criteria can be pre-set for different user groups. Then, based on the currently received screening criteria, the user group corresponding to the current screening criteria can be selected.

[0033] User behavior data can refer to various data related to user behavior, which can be determined based on actual application requirements or scenarios. User behavior can also be flexibly configured based on actual application requirements or scenarios. For example, user behavior includes but is not limited to browsing behavior, click behavior, transaction behavior, etc. For another example, user behavior includes but is not limited to one or more browsing behaviors of specified information, one or more transaction behaviors of specified information, etc.

[0034] For example, when user behavior includes browsing behavior, user behavior data may include but is not limited to: number of browsing times, browsing duration, browsing time, etc. When user behavior includes transaction behavior, user behavior data may include but is not limited to: number of transactions, transaction time, etc.

[0035] In this embodiment, the execution subject of the method for pushing information (such as Figure 1 Server shown) can be accessed from local or other storage devices (such as Figure 1The terminal devices 101, 102, 103 shown or the connected database, etc.) obtain the behavioral data corresponding to each user in the user group.

[0036] In this embodiment, the classification identifier can be used to indicate the classification method for the user group. Specifically, by classifying the user group, the user group can be divided into several sub-user groups, thereby forming sub-user clusters. Generally, different sub-user clusters can be obtained by classifying the user group using different classification methods.

[0037] A classification identifier set can be composed of different classification identifiers. The classification identifier set can be determined based on actual application requirements. For example, a classification identifier set can be pre-specified by a technician, etc. For another example, a classification identifier set can be composed of classification identifiers corresponding to all classification methods for a user group, or it can be composed of classification identifiers corresponding to only a subset of classification methods for a user group.

[0038] In this embodiment, the execution subject may obtain the classification identification set from a local or other storage device, or may receive the classification identification set input by its user.

[0039] Step 202 : for a classification identifier in the classification identifier set, determine the difference between the behavior data of the sub-user groups in the sub-user cluster corresponding to the classification identifier based on the behavior data of the users in the user group.

[0040] In this embodiment, the difference degree corresponding to each classification identifier can be used to represent the degree of difference between the behavior data corresponding to each sub-user group in the sub-user group corresponding to the classification identifier. The behavior data of the sub-user group can refer to the behavior data corresponding to each user in the sub-user group.

[0041] For example, for a classification identifier, the degree of difference between each pair of sub-user groups within the sub-user cluster corresponding to the classification identifier can be determined first, and then the average of all the differences can be calculated as the degree of difference corresponding to the classification identifier. The degree of difference between any two sub-user groups can be represented by the number of different user behavior data points in the two sub-user groups.

[0042] Step 203: Select a classification identifier from the classification identifier set as a target classification identifier according to the corresponding difference degree.

[0043] In this embodiment, various methods can be flexibly adopted to select a classification identifier from the classification identifier set according to actual application scenarios. For example, a classification identifier whose corresponding difference is greater than a preset difference threshold can be selected from the classification identifier set.

[0044] Step 204: Push information to each sub-user group in the sub-user group corresponding to the target classification identifier.

[0045] In this embodiment, information can be pushed to each sub-user group within the sub-user cluster corresponding to the target classification identifier. Specifically, the same or different information can be pushed to each sub-user group based on the behavioral data corresponding to each sub-user group. The content of the information pushed to each sub-user group can be determined based on actual application requirements and / or the behavioral data of users in the sub-user group.

[0046] Optionally, the classification identifier corresponding to the maximum difference can be selected from the classification identifier set as the target classification identifier, and information can then be pushed to each sub-user group within the sub-user group corresponding to the target classification identifier. In this case, the behavioral data of each sub-user group obtained by dividing the user group according to the classification method indicated by the target classification identifier will have the greatest difference. Based on these differences, information can be pushed to each sub-user group in a targeted manner, thus achieving personalized information push for users in the user group.

[0047] In some optional implementations of this embodiment, the difference degree corresponding to each classification identifier may be determined based on the behavioral data of users in the user group through the following steps:

[0048] Step 1: for a sub-user group in the sub-user group corresponding to the classification identifier, a behavior index value of the sub-user group is determined based on the behavior data of users in the sub-user group.

[0049] In this step, the behavioral indicators for a sub-user group can refer to behavioral indicators set for user behavior, which can be pre-set by technical personnel based on actual application requirements. For example, behavioral indicators include, but are not limited to, click-through rate, conversion rate, retention rate, etc. The behavioral indicator values for a sub-user group can generally be determined by statistical analysis of the behavioral data of each user in the sub-user group.

[0050] Optionally, the behavior indicator value for a sub-user group can refer to the behavior indicator value corresponding to a target time period. The target time period can be set based on specific application requirements. For example, the behavior indicator value can refer to the click-through rate, conversion rate, or retention rate of users within the past week, as determined by statistical analysis. This allows for more flexible and accurate determination of the behavior indicator value for a sub-user group based on the actual behavioral data collected from users within the user group.

[0051] Step 2: Determine the difference degree corresponding to the classification identifier based on the behavior indicator values corresponding to each sub-user group corresponding to the classification identifier.

[0052] In this step, after obtaining the behavior indicator values corresponding to each sub-user group corresponding to the classification identifier, various methods can be flexibly used to determine the degree of difference corresponding to the classification identifier. For example, the variance or standard deviation of the behavior indicator values corresponding to each sub-user group can be calculated, and the obtained variance or standard deviation can be used to represent the degree of difference corresponding to the classification identifier.

[0053] Because the behavioral index values of a user group can reflect some attributes of the users within the user group. For example, click-through rate and conversion rate can reflect user preferences, etc., determining the degree of difference corresponding to a classification identifier based on the behavioral index values of each corresponding sub-user group can more accurately represent the differences in attributes such as preferences of users within each sub-user group corresponding to the classification identifier, thereby facilitating the provision of differentiated services to users with different attributes, thereby improving service quality.

[0054] Optionally, the difference degree corresponding to each classification identifier may be determined according to the behavior indicator values corresponding to each sub-user group corresponding to each classification identifier through the following steps:

[0055] Step 1: Arrange the behavior indicator values corresponding to the sub-user groups corresponding to the classification identifier in order to obtain a behavior indicator value sequence.

[0056] In this step, the behavior index values corresponding to the sub-user groups may be arranged in order according to the magnitude of the corresponding behavior index values. For example, the behavior index values corresponding to the sub-user groups may be arranged in ascending order from smallest to largest.

[0057] Step 2: Determine the difference between adjacent behavior indicator values in the behavior indicator value sequence.

[0058] In this step, the difference between each pair of adjacent behavior index values in the index value sequence can be calculated. For example, if the index value sequence includes a first index value, a second index value, and a third index value, the difference between the first index value and the second index value, and the difference between the second index value and the third index value, can be determined. Generally, the difference can be non-negative.

[0059] Step three: determine the degree of difference corresponding to the classification identifier based on the determined difference value.

[0060] In this step, various methods can be used to determine the degree of difference corresponding to the classification identifier based on the obtained differences. For example, the variance, average difference, or sum of the obtained differences can be calculated, and the calculated variance, average difference, or sum can be used to represent the degree of difference corresponding to the classification identifier.

[0061] For another example, for each set of two adjacent behavior index values, the adjacent difference ratio of the two behavior index values in the set can be calculated, where the adjacent difference ratio can represent the ratio of the difference between the two behavior index values to the larger behavior index value. Then, the sum of the adjacent difference ratios corresponding to each set of adjacent behavior index values can be calculated, and the calculated sum can be used to represent the degree of difference corresponding to the classification identifier.

[0062] The adjacent difference ratio can be used to more accurately represent the difference between users in two sub-user groups, thereby helping to more accurately represent the difference between the sub-user groups corresponding to each classification identifier, thereby improving the accuracy of the difference degree of the classification identifier.

[0063] In some optional implementations of this embodiment, the classification method indicated by the classification identifier in the classification identifier set is used to classify the user group according to the time characteristics or frequency characteristics of the user's target behavior.

[0064] The target behavior can be any of a user's behaviors, which can be predetermined by technical personnel based on actual application requirements. A time feature can refer to a feature related to the time at which the user's target behavior occurs. For example, a time feature can be directly characterized by the time at which the user's target behavior occurs. A frequency feature can refer to a feature related to the frequency with which the user's target behavior occurs. For example, a frequency feature can be characterized by the frequency with which the user performs the target behavior.

[0065] Optionally, the time feature can be represented by the time difference between the most recent occurrence of the user's target behavior and the target time. The target time can be flexibly set based on actual application requirements or application scenarios. For example, the target time can be the current time or a preset time.

[0066] When categorizing user groups based on time characteristics, users within a user group can be categorized by dividing the time into segments. For example, if the time characteristic indicates the number of days between a user's most recent browsing behavior and a preset date, the user group can be categorized differently by dividing the number of days between the user's most recent browsing behavior and the preset date into different groups.

[0067] For example, the classification identifier may be (3, 7, 14), which indicates a classification method that can divide the user group into four categories. Among them, users whose last browsing behavior is within 3 days of the preset date are in the first category, users whose last browsing behavior is between 3 and 7 days of the preset date are in the second category, users whose last browsing behavior is between 7 and 14 days of the preset date are in the third category, and users whose last browsing behavior is more than 14 days of the preset date are in the fourth category.

[0068] Optionally, the frequency feature can be characterized by the frequency of users performing the target behavior within a target time period. The target time period can be flexibly set according to specific application requirements. For example, the target time period can be within the past week or within the past year.

[0069] When categorizing user groups based on frequency characteristics, users within a user group can be categorized by frequency segmentation. For example, if the frequency characteristic indicates the number of transactions a user has performed in the past year, different categorizations of the user group can be achieved by segmenting the number of transactions performed by the user in the past year.

[0070] For example, the classification identifier may be (5, 20), which indicates a classification method that can divide the user group into three categories. Among them, users who have conducted transactions less than 5 times in the past year are in the first category, users who have conducted transactions between 5 and 20 times in the past year are in the second category, and users who have conducted transactions more than 20 times in the past year are in the third category.

[0071] At this time, the obtained behavior data of users in the user group may include data representing the time characteristics or frequency characteristics of the target behavior of the users.

[0072] In some optional implementations of this embodiment, user groups can be determined based on target objects. For example, a relationship between user groups and target objects can be pre-set. In this case, the corresponding user group can be searched based on the target object. Specifically, based on a pre-specified target object or a target object indicated by a currently received instruction, the user group corresponding to the target object can be determined, thereby obtaining the behavioral data of users in the user group. In this case, the behavioral data of users in the user group can refer to the users' behavioral data with respect to the target object. This allows for the classification of user groups related to the target object.

[0073] The target object may be any object. For example, the target object may be various goods or services. For example, when a specified category is used to represent the target object, the target object is all goods or services included in the category.

[0074] Optionally, an information push request for a target object can be received, and then the behavior data of users in the user group regarding the target object can be obtained. Based on the obtained behavior data, a target classification identifier is selected from the classification identifier set to push relevant information of the target object to each sub-user group in the sub-user cluster corresponding to the target classification identifier.

[0075] The target object can be any object. The user group can be composed of all or some users who have a target behavior for the target object. The target behavior can be set according to the specific application scenario. This allows for flexible classification of the user group of the target object, thereby enabling personalized push of relevant information for the target object.

[0076] At this time, the corresponding relationship between the target object and the target classification identifier may be further stored.

[0077] In some optional implementations of this embodiment, users in a user group belong to the same user category, and the user category is determined based on the behavioral data of the users in the user group. In this case, the classification identifier in the classification identifier set is used to indicate that the users should be further classified based on the user's behavioral data to determine the subcategory to which each user in the user group belongs.

[0078] For example, users who have been pushed information about the target object belong to the first category, users who have browsed the information about the target object belong to the second category, users who have executed transactions for the target object belong to the third category, and users who have executed transactions for the target object more than a preset threshold belong to the fourth category. In this case, the target object user group can be divided into four categories according to this classification method.

[0079] Then, for each category of user group, the above steps 201-204 may be executed to determine a classification method for the sub-user group, and then targeted information push may be performed according to the determined classification method.

[0080] In many cases, the number of users is enormous, and even after categorizing them, each user group still contains a very large number of users. In this case, by further segmenting the user groups within each category, we can push information more specifically to each user, improving the accuracy of information push.

[0081] In some optional implementations of this embodiment, after a target classification identifier is selected from the classification identifier set, classification result information of the classification method indicated by the target classification identifier for the user group may be further displayed.

[0082] The classification result information may refer to various information related to the classification result. For example, the classification result information may include sub-user clusters obtained by dividing the user group according to the target classification identifier.

[0083] Optionally, the classification result information may include attribute information of the sub-user cluster corresponding to the target classification identifier. The attribute information of the sub-user cluster may refer to information used to describe various characteristics of the sub-user cluster. For example, the attribute information of the sub-user cluster may include attribute information corresponding to each sub-user group therein. The attribute information of the sub-user group may refer to information used to describe various characteristics of the sub-user group. For example, the attribute information of the sub-user group may include the number of users included in the sub-user group, the ratio of the number of users included in the sub-user group to the total number of users included in the sub-user cluster, and so on.

[0084] Optionally, the attribute information of the sub-user clusters may further include behavior index values corresponding to each sub-user cluster, differences corresponding to the sub-user clusters, and the like.

[0085] In addition, various data such as the behavior data of users in the user group can be pre-stored in the database and updated in real time. The above steps 201-204 can be performed in real time based on the stored data or can be performed offline.

[0086] Depending on actual application requirements and scenarios, the database can store various data about users within a user group. For example, when a user engages in a transaction, data such as the transaction time, transaction amount, and transaction frequency within the past year can be stored alongside the transaction time. In some cases, transaction time can also include transaction start time, transaction end time, and transaction duration.

[0087] Optionally, the classification identifier set may be composed of classification identifiers corresponding to all or selected classification methods for the user group. In this case, a classification identifier corresponding to a better classification method may be selected from the classification identifier set to assist in information push for users in the user group.

[0088] Continue to see Figure 3 , Figure 3 FIG. 3 is an exemplary application scenario 300 of the method for pushing information according to this embodiment. Figure 3 In the application scenario, the execution entity 301 may receive an input book category "A", and then obtain user identifiers that have interacted with books under category "A" from the database 302 to form a user group 303. Each user identifier may be used to identify a user.

[0089] The execution entity 301 may also receive two input classification identifiers: (3, 10) and (7, 14). The classification identifier (3, 10) is used to classify users into three categories based on the number of days between the last time the user browsed books in category "A" a week ago (i.e., the browsing interval): users with a browsing interval of less than 3 days, users with a browsing interval of between 3 and 10 days, and users with a browsing interval of more than 10 days.

[0090] The classification identifier (7,14) is used to divide users into three categories according to the number of days between their browsing intervals: browsing intervals within 7 days, browsing intervals between 7 and 14 days, and browsing intervals exceeding 14 days.

[0091] The number of days between browsing and purchasing behavior of each user is also stored in the database 302. The purchasing behavior is used to indicate whether the user has purchased books of category "A" in the past week.

[0092] For the classification identifier (3,10), the user group 303 is divided into a first sub-user group, a second sub-user group, and a third sub-user group according to the number of days between users' browsing intervals stored in the database 302. Then, based on the purchase behavior of the users stored in the database 302, the conversion rate of each sub-user group is determined by the ratio of the number of users with purchase behavior in each sub-user group to the total number of users included in the sub-user group. Then, the difference corresponding to the classification identifier (3,10) is calculated based on the conversion rates corresponding to the first sub-user group, the second sub-user group, and the third sub-user group. The specific calculation method can be referred to above. Figure 2 The relevant descriptions in the embodiments will not be repeated here.

[0093] Similarly, for classification identifier (7, 14), user group 303 is divided into a fourth sub-user group, a fifth sub-user group, and a sixth sub-user group based on the number of days between users' browsing intervals stored in database 302. Then, based on the users' purchasing behavior stored in database 302, the conversion rate of each sub-user group is determined by the ratio of the number of users with purchasing behavior in each sub-user group to the total number of users in that sub-user group. The degree of difference corresponding to classification identifier (7, 14) is then calculated based on the conversion rates corresponding to the first, second, and third sub-user groups, respectively.

[0094] Based on the differences between the classification identifiers (3, 10) and (7, 14), the classification identifier (3, 10) corresponding to the larger difference is selected. Different push messages are then set for the first sub-user group, the second sub-user group, and the third sub-user group, respectively. The first push message is pushed to users in the first sub-user group, the second push message is pushed to users in the second sub-user group, and the third push message is pushed to users in the third sub-user group.

[0095] In the existing technology, each user is usually assigned a corresponding user tag, and users with the same user tag are grouped into corresponding user groups. Then, new user groups are generated by operations such as intersection, union, and difference of user groups to correspond to the intersection, union, and difference of user tags, and information is pushed to the new user groups.

[0096] The method provided by the above-described embodiments of the present disclosure selects a classification identifier with a degree of differentiation that meets the requirements, and can then divide a user group into several sub-user groups according to the classification method corresponding to the classification identifier, thereby achieving flexible classification of the user group and distinguishing users with significantly different behavioral data within the user group. Based on this, information can be further pushed to each of the divided sub-user groups, achieving personalized information push for users within the user group.

[0097] After displaying the classification result information of the classification method indicated by the target classification identifier for the user group, the target classification identifier can be further updated according to the needs. Figure 4 , which shows a process 400 of an embodiment for updating a target classification identifier. The process 400 of the method for pushing information includes the following steps:

[0098] Step 401: In response to receiving a modification request for a target classification identifier, a modified classification identifier is received.

[0099] In this embodiment, after browsing the classification result information corresponding to the target classification identifier, the user can send a modification request for requesting modification of the target classification identifier to the execution entity as needed. At the same time, the user can send the classification identifier that he or she desires to modify to the execution entity.

[0100] Step 402: Determine and display the classification result information corresponding to the modified classification identifier.

[0101] In this embodiment, the execution subject can use Figure 2 The method disclosed in the corresponding embodiment determines the classification result information corresponding to the modified classification identifier. Figure 2 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0102] Optionally, the execution entity or other electronic device may pre-use Figure 2 The method disclosed in the corresponding embodiment determines and stores the classification result information corresponding to the modified classification identifier. At this time, the execution subject can directly obtain and display the pre-stored classification result information corresponding to the modified classification identifier.

[0103] Step 403: in response to receiving the update request for the target classification identifier, update the target classification identifier using the modified classification identifier.

[0104] In this embodiment, the user can compare the classification result information corresponding to the original target classification identifier and the modified classification identifier. If the classification result information corresponding to the modified classification identifier meets the expectations, the user can send a request to the execution entity to request an update of the target classification identifier. The execution entity can then update the original target classification identifier to the user-modified classification identifier.

[0105] Alternatively, if the classification result information corresponding to the modified classification identifier does not meet expectations, the original target classification identifier may be kept unchanged. Alternatively, the above steps 401-403 may be continued until the target user identifier is updated to a classification identifier that meets expectations.

[0106] Optionally, after updating the target classification identifier, information can be further pushed to each sub-user group in the sub-user group corresponding to the target classification identifier. Figure 2 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0107] It should be noted that the contents not specifically described in this embodiment can be referred to Figure 2 The relevant descriptions in the corresponding embodiments will not be repeated here.

[0108] The method for pushing information provided by the above-mentioned embodiments of the present disclosure can select a classification identifier with better classification results from a classification identifier set, and display the classification result information of the selected classification identifier to the user. At the same time, the user can modify the classification identifier according to needs, and view the classification result information corresponding to the modified classification identifier. Then, by comparing the classification result information corresponding to different classification identifiers, the classification identifier whose corresponding classification result information is more in line with the expectations can be selected as the target classification identifier, and more accurate information push can be achieved based on the target classification identifier.

[0109] Further references Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for pushing information. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0110] like Figure 5 As shown, the apparatus 500 for pushing information provided in this embodiment includes an acquisition unit 501, a determination unit 502, a selection unit 503, and a push unit 504. The acquisition unit 501 is configured to acquire behavioral data of users in a user group and determine a classification identifier set for the user group, wherein the classification identifier in the classification identifier set is used to indicate a classification method for classifying the user group to obtain sub-user clusters; the determination unit 502 is configured to determine, based on the behavioral data of users in the user group, the difference between the behavioral data of sub-user groups in the sub-user cluster corresponding to the classification identifier in the classification identifier set; the selection unit 503 is configured to select a classification identifier from the classification identifier set as a target classification identifier based on the corresponding difference; and the push unit 504 is configured to push information to each sub-user group in the sub-user cluster corresponding to the target classification identifier.

[0111] In this embodiment, in the apparatus for pushing information 500, the specific processing of the obtaining unit 501, the determining unit 502, the selecting unit 503 and the pushing unit 504 and the technical effects thereof can be referred to in the respective Figure 2 The relevant descriptions of step 201, step 202, step 203 and step 204 in the corresponding embodiment are not repeated here.

[0112] In some optional implementations of this embodiment, the above-mentioned determination unit 502 is further configured to: determine the behavior index value of the sub-user group in the sub-user cluster corresponding to the classification identifier based on the behavior data of the users in the sub-user group; and determine the difference degree corresponding to the classification identifier based on the behavior index values corresponding to each sub-user group corresponding to the classification identifier.

[0113] In some optional implementations of this embodiment, the above-mentioned determination unit 502 is further configured to: arrange the behavior indicator values corresponding to each sub-user group corresponding to the classification identifier in order to obtain a behavior indicator value sequence; determine the difference between adjacent behavior indicator values in the behavior indicator value sequence; and determine the degree of difference corresponding to the classification identifier based on the determined difference.

[0114] In some optional implementations of this embodiment, the classification method indicated by the classification identifier in the above classification identifier set is used to classify the user group according to the time characteristics or frequency characteristics of the user's target behavior.

[0115] In some optional implementations of this embodiment, the above-mentioned time feature is used to characterize the time difference between the most recent occurrence time of the user's target behavior and the target time, and the frequency feature is used to characterize the occurrence frequency of the user's target behavior within the target time period.

[0116] In some optional implementations of this embodiment, the above-mentioned device 500 for pushing information also includes a receiving unit (not shown in the figure), which is configured to receive an information push request for a target object; and the above-mentioned acquisition unit 501 is further configured to obtain behavioral data of users in the user group regarding the target object.

[0117] In some optional implementations of this embodiment, the users in the above-mentioned user group belong to the same user category, wherein the user category is determined based on the behavioral data of the users in the user group; and the classification identifier in the above-mentioned classification identifier set is used to indicate the subclassification of the user group to determine the user subcategory to which the users in the user group belong.

[0118] In some optional implementations of this embodiment, the above-mentioned device 500 for pushing information also includes a display unit (not shown in the figure), which is configured to display the classification result information of the classification method indicated by the target classification identifier for the user group, wherein the classification result information includes attribute information of the sub-user cluster corresponding to the target classification identifier.

[0119] In some optional implementations of this embodiment, the above-mentioned device 500 for pushing information also includes an update unit (not shown in the figure), which is configured to: receive a modified classification identifier in response to receiving a modification request for the target classification identifier; determine and display the classification result information corresponding to the modified classification identifier; and update the target classification identifier using the modified classification identifier in response to receiving an update request for the target classification identifier.

[0120] The apparatus provided by the above-mentioned embodiment of the present disclosure obtains the behavioral data of users in a user group through an acquisition unit, and determines a classification identifier set for the user group, wherein the classification identifier in the classification identifier set is used to indicate the classification method for classifying the user group to obtain sub-user clusters; the determination unit determines the difference between the behavioral data of the sub-user groups in the sub-user cluster corresponding to the classification identifier in the classification identifier set based on the behavioral data of the users in the user group; the selection unit selects the classification identifier from the classification identifier set as the target classification identifier based on the corresponding difference; the push unit pushes information to each sub-user group in the sub-user cluster corresponding to the target classification identifier, thereby selecting a classification identifier with a difference that meets the requirements, and dividing the user group into several sub-user groups according to the classification method corresponding to the classification identifier, thereby realizing flexible classification of the user group. At the same time, information can also be pushed to each of the divided sub-user groups separately, realizing different information push to users in the user group.

[0121] Reference below Figure 6 , which shows an electronic device (eg, Figure 1 A schematic diagram of the structure of the server in (600). Figure 6 The server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0122] like Figure 6As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device 600 are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0123] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 6 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0124] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0125] It should be noted that the computer-readable medium described in the embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device. In the embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0126] The computer-readable medium may be included in the server, or may exist independently and not incorporated into the server. The computer-readable medium carries one or more programs that, when executed by the server, cause the server to: obtain behavioral data of users in a user group, and determine a classification identifier set for the user group, wherein the classification identifier in the classification identifier set is used to indicate a classification method for classifying the user group to obtain sub-user clusters; for a classification identifier in the classification identifier set, determine, based on the behavioral data, the degree of difference between the behavioral data of sub-user groups in the sub-user cluster corresponding to the classification identifier; select a classification identifier from the classification identifier set as a target classification identifier based on the corresponding degree of difference; and push information to each sub-user group in the sub-user cluster corresponding to the target classification identifier.

[0127] Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0128] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0129] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The units described may also be provided in a processor. For example, they may be described as: a processor including an acquisition unit, a determination unit, a selection unit, and a push unit. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the push unit may also be described as a "unit for pushing information to each sub-user group in the sub-user cluster corresponding to the target classification identifier."

[0130] The above description is merely a preferred embodiment of the present disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also encompass other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by mutually replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for pushing information, comprising: Obtaining behavioral data of users in a user group, and determining a classification identifier set for the user group, wherein the classification identifier in the classification identifier set is used to indicate a classification method for classifying the user group to obtain sub-user clusters; For a classification identifier in the classification identifier set, determining, based on the behavior data, a degree of difference between the behavior data of sub-user groups in the sub-user cluster corresponding to the classification identifier, wherein the degree of difference corresponding to each classification identifier is used to represent the degree of difference between the behavior data corresponding to each sub-user group in the sub-user cluster corresponding to the classification identifier, where the behavior data of a sub-user group refers to the behavior data corresponding to each user in the sub-user group; Selecting a classification identifier from the classification identifier set as a target classification identifier according to the corresponding difference degree; Push information to each sub-user group in the sub-user group corresponding to the target classification identifier.

2. The method according to claim 1, wherein Determining, based on the behavior data, the difference between the behavior data of the sub-user groups in the sub-user cluster corresponding to the classification identifier includes: For a sub-user group in the sub-user cluster corresponding to the classification identifier, determining a behavior index value of the sub-user group based on the behavior data of users in the sub-user group; The degree of difference corresponding to the classification identifier is determined according to the behavior indicator values corresponding to each sub-user group corresponding to the classification identifier.

3. The method according to claim 2, wherein: The determining the difference degree corresponding to the classification identifier according to the behavior indicator values corresponding to each sub-user group in the sub-user group corresponding to the classification identifier includes: Arrange the behavior indicator values corresponding to the sub-user groups corresponding to the classification identifier in order to obtain a behavior indicator value sequence; determining a difference between adjacent behavior indicator values in the behavior indicator value sequence; According to the difference, the degree of difference corresponding to the classification identifier is determined.

4. The method according to claim 1, wherein The classification method indicated by the classification identifier in the classification identifier set is used to classify the user group according to the time characteristics or frequency characteristics of the user's target behavior.

5. The method according to claim 4, wherein The time feature is used to characterize the time difference between the most recent occurrence time of the user's target behavior and the target time, and the frequency feature is used to characterize the frequency of the user performing the target behavior within the target time period.

6. The method according to claim 1, wherein The method further comprises: Receive information push requests for target objects; and The obtaining of the behavior data of users in the user group includes: Obtaining behavioral data of users in the user group with respect to the target object.

7. The method according to any one of claims 1 to 6, wherein: The users in the user group belong to the same user category, wherein the user category is determined based on behavioral data of the users in the user group; and The classification identifiers in the classification identifier set are used to indicate a subclassification of the user group to determine the user subcategory to which the users in the user group belong.

8. The method according to any one of claims 1 to 6, wherein: The method further comprises: The classification result information of the classification method indicated by the target classification identifier for the user group is displayed, wherein the classification result information includes attribute information of the sub-user cluster corresponding to the target classification identifier.

9. The method according to claim 8, wherein After displaying the classification result information of the classification method indicated by the target classification identifier for the user group, the method further includes: In response to receiving a modification request for the target classification identifier, receiving a modified classification identifier; Determine and display the classification result information corresponding to the modified classification identifier; In response to receiving an update request for the target classification identifier, the target classification identifier is updated using the modified classification identifier.

10. A device for pushing information, wherein: The device comprises: an acquisition unit configured to acquire behavioral data of users in a user group and determine a classification identifier set for the user group, wherein the classification identifier in the classification identifier set is used to indicate a classification method for classifying the user group to obtain sub-user clusters; a determining unit configured to determine, for a classification identifier in the classification identifier set, based on the behavior data, a degree of difference between behavior data of sub-user groups in the sub-user cluster corresponding to the classification identifier, wherein the degree of difference corresponding to each classification identifier is used to represent a degree of difference between behavior data corresponding to each sub-user group in the sub-user cluster corresponding to the classification identifier, and the behavior data of a sub-user group refers to behavior data corresponding to each user in the sub-user group; a selection unit configured to select a classification identifier from the classification identifier set as a target classification identifier based on the corresponding difference degree; The pushing unit is configured to push information to each sub-user group in the sub-user cluster corresponding to the target classification identifier.

11. A server comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 9.

12. A computer-readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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