CN111368050B - Method and device for pushing document pages - Google Patents
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Abstract
The embodiment of the application provides a method and a device for pushing a document page. The method comprises the following steps: receiving a first set of document pages provided by a third party platform according to user intent; determining the association degree of the document pages in the first document page set and the user intention through a user behavior characteristic model, wherein the association degree is used for representing the degree of the document pages in the first document page set conforming to the user intention; filtering the document pages with the association degree lower than a preset threshold value in the first document page set to obtain a second document page set; pushing the document pages in the second document page set to a user. According to the technical scheme, the accuracy of pushing the document pages can be improved.
Description
Technical Field
The application relates to the technical field of computers and the Internet, in particular to a method and a device for pushing document pages.
Background
In a pushing scenario of a document page, for example, in a scenario of pushing the document page for a user through an application platform, generally, the application platform requests a third party platform to provide the application platform with the document page pushed to the user by distributing the identified user intention and the user intention requirement to the third party platform. However, in the prior art, a situation of pushing some wrong document pages to a user often happens, so how to improve the accuracy of pushing the document pages is a technical problem to be solved.
Disclosure of Invention
The embodiment of the application provides a pushing method and device of a document page, a computer readable medium and electronic equipment, and further can improve the pushing accuracy of the document page at least to a certain extent.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned in part by the practice of the application.
According to one aspect of the embodiment of the application, there is provided a method for pushing a document page, including: receiving a first set of document pages provided by a third party platform according to user intent; determining the association degree of the document pages in the first document page set and the user intention through a user behavior characteristic model, wherein the association degree is used for representing the degree of the document pages in the first document page set conforming to the user intention; filtering the document pages with the association degree lower than a preset threshold value in the first document page set to obtain a second document page set; pushing the document pages in the second document page set to a user.
According to an aspect of an embodiment of the present application, there is provided a pushing device for a document page, including: a receiving unit for receiving a first set of document pages provided by a third party platform according to user intention; a first determining unit, configured to determine, through a user behavior feature model, a degree of association between a document page in the first document page set and the user intention, where the degree of association is used to characterize a degree to which the document page in the first document page set meets the user intention; a filtering unit, configured to filter document pages in the first document page set, where the association degree of the document pages is lower than a predetermined threshold value, to obtain a second document page set; and the pushing unit is used for pushing the document pages in the second document page set to a user.
In some embodiments of the present application, based on the foregoing solution, the apparatus further includes a first obtaining unit, configured to obtain, before receiving the first set of document pages provided by the third party platform according to the user intention, keywords input by the user for searching the document pages, so as to determine the user intention of the user; and the sending unit is used for sending the user intention to a third party platform so as to request the third party platform to provide the first document page set.
In some embodiments of the present application, based on the foregoing, the apparatus further comprises a second acquisition unit for acquiring a set of behavioral data for the full amount of users over a predetermined time interval of history; a construction unit, configured to construct an heterograph network according to the behavior data set; and the training unit is used for training a machine learning model through the heterogeneous graph network to obtain the user behavior characteristic model.
In some embodiments of the present application, based on the foregoing solution, the second obtaining unit is configured to: a set of behavioral data is obtained for a full amount of users when entering keywords over a historically predetermined time interval, when clicking on a document page, and when operating within the document page.
In some embodiments of the present application, based on the foregoing scheme, the building unit is configured to: determining the operation behaviors of the keywords, the document pages and the total users in the document pages in the behavior data set as heterogeneous graph network nodes; determining the association between keywords, the association between keywords and document pages and the association between document pages and the operation behaviors of the full-scale users in the document pages in the behavior data set as heterogeneous graph network connection edges; and constructing the heterograph network based on the heterograph network node and the heterograph network connecting edge.
In some embodiments of the present application, based on the foregoing solution, the apparatus further includes a second determining unit configured to determine, before the heterogeneous graph network is constructed based on the heterogeneous graph network node and the heterogeneous graph network connection edge, feature vectors of the respective heterogeneous graph network nodes based on contents of keywords in the behavior data set, contents of a document page, and operational behavior features of a full amount of users inside the document page; and determining the length of each connecting edge of the heterogeneous graph network based on the association times between the keywords in the behavior data set, the association times between the keywords and the document pages and the association times between the document pages and the operation behaviors of the whole user in the document pages, wherein the length of each connecting edge is used for measuring the association tightness degree between different heterogeneous graph network nodes.
In some embodiments of the present application, based on the foregoing solution, the apparatus further includes a feedback unit configured to, after filtering the document pages in the first set of document pages that have the relevance below a predetermined threshold, feed back the document pages that have the relevance below the predetermined threshold to a corresponding third party platform, so as to request the third party platform to change the provided document pages.
In some embodiments of the present application, based on the foregoing solution, the second obtaining unit is configured to: after pushing the document pages in the second document page set to the user, acquiring feedback behavior data of the user for the pushed document pages; the training unit is configured to: and training the user behavior characteristic model based on the feedback behavior data of the user.
In some embodiments of the present application, the document page comprises an applet page based on the foregoing scheme.
According to an aspect of the embodiments of the present application, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a pushing method of a document page as described in the above embodiments.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; and a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the method for pushing document pages as described in the above embodiments.
In the technical scheme provided by some embodiments of the present application, firstly, a user behavior feature model is used, then, through the user behavior feature model, the association degree between the document pages in the first document page set and the user intention is determined in a first document page set provided by a third party platform according to the user intention, and the document pages with the association degree lower than a preset threshold value in the first document page set are filtered to obtain a second document page set, and finally, the document pages in the second document page set are pushed to the user. The degree of relevance is used for representing the degree that the document pages in the first document page set accord with the intention of the user, and the document pages with the relevance lower than a preset threshold value are filtered, so that the document pages pushed to the user accord with the intention and the requirement of the user, and further the accuracy of pushing the document pages can be improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 shows a schematic diagram of an exemplary system architecture to which the technical solutions of embodiments of the present application may be applied;
FIG. 2 illustrates an application scenario diagram of a pushing method of a document page according to one embodiment of the present application;
FIG. 3 illustrates a flow chart of a method of pushing a document page according to one embodiment of the present application;
FIG. 4 illustrates a method flow diagram prior to receiving a first set of document pages provided by a third party platform according to user intent in accordance with one embodiment of the present application;
FIG. 5 illustrates a flow chart of a method of obtaining a user behavior feature model according to one embodiment of the present application;
FIG. 6 illustrates a detailed flow diagram of constructing an heterograph network according to one embodiment of the present application;
FIG. 7 illustrates a method flow diagram prior to building an heterograph network in accordance with one embodiment of the present application;
FIG. 8 illustrates a schematic diagram of an iso-patterning network, according to one embodiment of the present application;
FIG. 9 illustrates a flow chart of a method after pushing document pages in a second set of document pages to a user according to one embodiment of the present application;
FIG. 10 illustrates a method flow diagram for pushing applet pages according to one embodiment of the application;
FIG. 11 illustrates a block diagram of a pushing device of a document page according to one embodiment of the present application;
fig. 12 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present application. One skilled in the relevant art will recognize, however, that the aspects of the application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
It should be noted that, before collecting relevant data (such as user behavior data) of a user and during collecting relevant data of a user, a prompt interface or a popup window may be displayed, where the prompt interface or the popup window is used to prompt the user to collect relevant data currently, so that the present application only starts to execute a relevant step of acquiring relevant data of the user after acquiring a confirmation operation sent by the user to the prompt interface or the popup window, otherwise (i.e. when a confirmation operation sent by the user to the prompt interface or the popup window is not acquired), ends a relevant step of acquiring relevant data of the user, i.e. does not acquire relevant data of the user. In other words, in the specific embodiments of the present application, related data such as user behavior is referred to, when the above embodiments of the present application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data is required to comply with related laws and regulations and standards of related countries and regions.
Fig. 1 shows a schematic diagram of an exemplary system architecture to which the technical solutions of the embodiments of the present application may be applied.
As shown in fig. 1, the system architecture may include a terminal device (such as one or more of the smartphone 101, tablet 102, and portable computer 103 shown in fig. 1, but of course, a desktop computer, etc.), a network 104, and a server 105. The network 104 is the medium used to provide communication links between the terminal devices and the server 105. The network 104 may include various connection types, such as wired communication links, wireless communication links, and the like.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server 105 may be a server cluster formed by a plurality of servers.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the objects so used may be interchanged where appropriate such that the embodiments of the present application described herein may be implemented in sequences other than those illustrated or described.
In one embodiment of the present application, the server 105 may receive a first document page set provided by a third party platform according to a user intention, then determine, through a user behavior feature model, a relevance between a document page in the first document page set and the user intention in the first document page set, filter a document page in the first document page set, where the relevance is lower than a predetermined threshold, obtain a second document page set, and finally send the document page in the second document page set to a terminal device, so as to push the document page to the user.
In another embodiment of the present application, the server 105 may also be configured to directly receive, by the terminal device, a first document page set provided by the third party platform according to the user intention, then determine, by using a user behavior feature model, a degree of association between a document page in the first document page set and the user intention in the first document page set, filter a document page in the first document page set, where the degree of association is lower than a predetermined threshold, to obtain a second document page set, and finally directly push, by using the terminal device, a document page in the second document page set to the user.
It should be noted that, the method for pushing the document page provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the device for pushing the document page is generally disposed in the server 105. However, in other embodiments of the present application, the terminal device may also have a similar function as the server, so as to execute the pushing scheme of the document page provided in the embodiments of the present application.
In addition, in the application, the type of the document page may be an applet page, a public number page, or a web page. It should be noted that the types of document pages listed above are merely exemplary, and those skilled in the art will appreciate that other types of document pages may be included in actual applications.
Fig. 2 shows a schematic application scenario of a pushing method of a document page according to an embodiment of the present application, and in particular, the application scenario shown in the figure is a scenario in which the pushing method of the document page proposed in the present application is applied to pushing an applet page for a user.
In the application scenario as shown in the figure, the user expresses his intention and demand by inputting 'ABC ocean park' in the search column of the APP application program, the background recognizes the intention of the user to order ABC ocean park scenic spot tickets, a third party platform (for example, a third party platform corresponding to '123 ticketing applet') for ticket operation provides an applet page (document page) for ordering ABC ocean park scenic spot tickets to the APP application background according to the intention of the user, and then the APP application background filters some applet pages which do not meet the intention of the user, pushes and displays the applet page meeting the intention of the user to the user through an APP interface (for example, the interface shown by 201 in fig. 2), and the user clicks one of the applet pages to enter the applet page (for example, the page shown by 202 in fig. 2) for ordering ABC ocean park scenic spot tickets, and confirms the order ticket through a series of operations.
The implementation details of the technical solutions of the embodiments of the present application are described in detail below:
according to a first aspect of the present disclosure, a method for pushing a document page is provided.
Referring to fig. 3, a flowchart of a method of pushing a document page according to one embodiment of the present application is shown, which may be performed by a device having a calculation processing function, such as the server 105 shown in fig. 1, or by a terminal device as shown in fig. 1. As shown in fig. 3, the pushing method of the document page at least includes steps 320 to 380:
in step 320, a first set of document pages provided by a third party platform according to user intent is received.
In the application, the third party platform generally establishes a corresponding document page in the background according to the requirements and the intentions of the user, and displays the corresponding document page to the user at the front end according to the service requirements and the intentions of the user.
However, it should be noted that in actual operation, a problem of document page errors may occur due to a fault of the third party platform, for example, a problem that a document page provided by the third party platform does not conform to a service requirement of a user. Taking the application scenario of the document page as the applet page shown in fig. 2 as an example, when the service requirement of the user is that ABC ocean park scenic spot tickets are to be ordered, the third party platform may provide the applet page for ordering other ocean park scenic spot tickets due to errors (the applet page for ordering ABC ocean park scenic spot tickets is created for the purpose of ordering other ocean park scenic spot tickets).
In one embodiment of the present application, the method as shown in FIG. 4 may also be implemented prior to receiving the first set of document pages provided by the third party platform according to the user's intent.
Referring to FIG. 4, a flowchart of a method, in particular steps 311 to 312, may be included before receiving a first set of document pages provided by a third party platform according to user intent, according to one embodiment of the present application:
step 311, obtaining keywords input by a user and used for searching document pages so as to determine the user intention of the user.
In the present application, the intention of the user may be determined by acquiring keywords input by the user, for example, in an application scenario in which the document page shown in fig. 2 is an applet page, the keyword "ABC ocean park" input by the user, and determining the intention of the user by the keywords may be implemented based on an existing semantic recognition technology.
Step 312, sending the user intention to a third party platform to request the third party platform to provide the first set of document pages.
In the present application, the identified user intention may be sent to the third party platform either simultaneously or separately to one third party platform, in order to provide the first set of document pages for requesting one or more third party platforms. It should be noted that the document page set may include a plurality of document pages, or may include only one document page.
With continued reference to FIG. 3, in step 340, a degree of association of the document pages in the first set of document pages with the user intent is determined by the user behavior feature model, the degree of association being used to characterize how well the document pages in the first set of document pages conform to the user intent.
In one embodiment of the present application, the user behavior feature model may be obtained by a method as shown in fig. 5.
Referring to fig. 5, a flowchart of a method for obtaining a user behavior feature model according to one embodiment of the present application is shown, and may specifically include steps 341 to 343:
step 341, obtaining a behavior data set of the full user in a historically predetermined time interval.
In a specific implementation of one embodiment, the behavior data set of the full-scale user over the historically predetermined time interval may be the behavior data set of the full-scale user when entering keywords, when clicking on a document page, and when operating inside the document page over the historically predetermined time interval.
Specifically, the predetermined time interval may be one week, one month, or one year. It is noted that the predetermined time interval may be arbitrary and is not limited to those exemplified previously.
It should be further noted that, the behavior data of the user when inputting the keywords may refer to the content of the keywords input by the user, for example, inputting "ABC ocean park", or inputting another keyword again after inputting one keyword, for example, inputting "ABC ocean park", or inputting "ABC ocean park" again, or selecting the associated keywords corresponding to the keywords input by the user, for example, after inputting "ABC", selecting the corresponding associated keywords "ABC ocean park" and "ABC ocean park" that are jumped out by the search bar: "ABC ocean park".
The behavior data of the user when clicking on a document page may refer to the user's selection behavior of the document page in the pushed document page, for example, in FIG. 2, after the user enters "ABC ocean park" in the search bar, the first document page is selected in the two pushed document pages.
Behavior data of a user when operating inside a document page may refer to specific operations within the document page after the user clicks on the document page into the document page, for example, in fig. 2, after the user clicks on the first document page, clicking on "confirm subscription" operation behavior within the document page.
And 342, constructing an heterograph network according to the behavior data set.
In a specific implementation of an embodiment, the building of the heterogeneous graph network according to the behavior data set may be implemented through steps as shown in fig. 6.
Referring to fig. 6, a detailed flow diagram of constructing an heterogenous network according to one embodiment of the present application is shown, which may specifically include steps 3421 to 3423:
step 3421, determining the keywords in the behavior data set, the document pages, and the operation behaviors of the full users in the document pages as heterogeneous network nodes.
Step 3422, determining the association between the keywords in the behavior data set, the association between the keywords and the document pages, and the association between the document pages and the operation behaviors of the full users inside the document pages as heterogeneous graph network connection edges.
It should be noted that, the association between keywords refers to the association between two keywords that are continuously input by the user, for example, after the user inputs one keyword "ABC ocean park" in the search bar, inputs or switches another keyword "ABC ocean park" in the search bar again, and then there is an association between the keyword "ABC ocean park" and the keyword "ABC ocean park"; the association between the keywords and the document pages means that after a keyword is input by a user, the user selects and clicks one document page in a set in the pushed document pages, and then an association is generated between the input keyword and the document page selected and clicked; the association between the document pages and the full-scale user's operational behaviors within the document pages refers to a series of operational behaviors performed by the user in selecting the clicked document page, and then a correlation is generated between the user selecting the clicked document page and the series of operational behaviors performed in selecting the clicked document page.
And 3423, constructing the heterograph network based on the heterograph network nodes and the heterograph network connection edges.
In a specific implementation of one embodiment, before constructing the heterogeneous graph network based on the heterogeneous graph network node and the heterogeneous graph network connection edge, the method shown in fig. 7 may also be implemented.
Referring to fig. 7, a flowchart of a method prior to constructing an heterogenous network according to one embodiment of the present application is shown, which may specifically include steps 331 to 332:
and 331, determining the feature vector of each heterogeneous network node based on the content of the keywords in the behavior data set, the content of the document page and the operation behavior features of the whole user in the document page.
And 332, determining the length of each connecting edge of the heterogeneous graph network based on the association times between the keywords in the behavior data set, the association times between the keywords and the document pages and the association times between the document pages and the operation behaviors of the whole user in the document pages, wherein the length of each connecting edge is used for measuring the association tightness between different heterogeneous graph network nodes.
In order to enable those skilled in the art to more understand the heterogeneous map network as described above, explanation will be made with reference to the structure of the heterogeneous map network shown in fig. 8.
Referring to fig. 8, a schematic diagram of the structure of an heterograph network is shown, according to one embodiment of the present application.
As shown, the structure of the heterograph network is composed of 3 kinds of nodes (keyword nodes, document page nodes, operation behavior nodes) and 3 kinds of (edge 1, edge 2, edge 3) connecting edges.
For the keyword node, the same keyword that the whole user has input or selected historically may be used as a keyword node, where the feature vector of each corresponding keyword node may be determined by the content of each keyword, for example, the word vector corresponding to the word "ABC ocean park" may be used as the feature vector corresponding to the keyword "ABC ocean park" node, and it should be noted that each keyword corresponds to a unique feature vector.
For the document page node, the document page is provided by a third party platform according to the intention of a user, and each document page provided by the third party platform can be used as a document page node, wherein the characteristic vector of each corresponding document page node can be determined through the content of each document page, for example, the corresponding title of the document page, the page data of the document and the like can be converted into a vector to be used as the characteristic vector of the document page node.
For the operation behavior node, the operation behavior of the user inside the document page may be regarded as the operation behavior node, wherein specific operation behaviors that can be inside the document page may be numbered, for example, 1 time in advance, 1 time in the page, 2 time in the page, 3 time in the confirmation, 4 time in the exit page, and the like. Further, a combination of numbers can be obtained for the specific operation behavior of the user in the document page, so that the combination of numbers can be used as a feature vector for representing the operation behavior of the user in the document page.
It should be noted that, through the feature vector corresponding to the operation behavior node, the operation behavior of the user in the document page can be determined, so as to determine the approval degree of the user for the pushed document page, for example, when the operation behavior after the user clicks into one document page is immediately exiting the page, it is described that the user does not approve the document page.
For the dot-dashed line edge shown in fig. 8, the dot-dashed line edge is a connecting edge between the keyword nodes, and is a two-way edge for associating keywords, wherein the length of the dot-dashed line edge is determined by the association times between the keywords and is used for representing the association tightness between the keywords. For example, the longer side 1 as shown, the more the number of associations between keyword 1 and keyword 2, i.e., the more the number of switches between keyword 1 and keyword 2.
For the long-dashed line edge shown in fig. 8, the long-dashed line edge is a connecting edge between the keyword node and the document page node, is a unidirectional edge, and points to the document page node from the keyword node, and the length of the long-dashed line edge is determined by the association times between the keyword node and the document page node and is used for representing the association tightness between the keyword node and the document page node. For example, the longer the edge 2 as shown, the more times a full amount of users select to click on the document page corresponding to the document page node 1 among the document pages recommended by the third party platform according to the corresponding intention of the keyword 1.
For the solid line edge shown in fig. 8, the solid line edge is a connecting edge between a document page node and an operation behavior node of a user in a document page, is a unidirectional edge, points to the operation behavior node from the document page node, and the length of the solid line edge is determined by the association times between the document page node and the operation behavior node and is used for representing the association tightness between the document page node and the operation behavior node. For example, the longer the edge 3 as shown, the greater the number of times that the full-scale user performs the corresponding operation behavior of the operation behavior node 1 in the document page corresponding to the document page node 1.
And step 343, training a machine learning model through the heterogeneous graph network to obtain the user behavior characteristic model.
Because the constructed heterograph network contains behavior information of the total users from keywords input to operations in the document page, the machine learning model is trained through the heterograph network, so that the machine learning model learns the behavior information of the total users in the heterograph network from keyword input to clicking of the document page and then to the operations in the document page, and the user behavior feature model is obtained.
In this application, regarding determining the association degree between the document pages in the first document page set and the user intention through the user behavior feature model, it may be understood as follows:
in one embodiment of the present application, for example, the degree of association of the user intent of the document page a with the keyword a may be calculated by the following procedure:
the method comprises the steps of firstly determining the association relation between a keyword a corresponding node and a document page A corresponding node through a user behavior feature model, then determining the association relation between the document page A corresponding node and the operation behavior corresponding nodes of the whole user in the document page A, and finally calculating the association relation between the keyword a corresponding node and the document page A corresponding node through the length of the connecting edge between the document page A corresponding node and each operation behavior node, so as to obtain the association relation of the user intention corresponding to the keyword a of the document page A.
For example, there are operation behaviors of 1000 users in the document page a corresponding to the keyword a historically, wherein, the operation behaviors of 750 users show the document page a approved for pushing, and the operation behaviors of 250 users show the document page a not approved for pushing, so that the association degree of the user intention of the document page a and the keyword a can be determined as follows: 750/1000=75%.
It will be appreciated by those skilled in the art that the degree of association may be used to characterize how well the document pages in the first set of document pages conform to the user's intent.
With continued reference to FIG. 3, in step 360, the document pages in the first set of document pages having the relevance below a predetermined threshold are filtered to obtain a second set of document pages.
In a specific implementation of step 360, for example, the document page set provided by the third party platform includes A, B, C, D document pages, where the association degrees between the 4 document pages and the user intention are a (90%), B (60%), C (80%), and D (40%), respectively, and if the association degree is 70% as a predetermined threshold value, after filtering the 4 document pages, a second document page set including the document page a (90%) and the document page C (80%) is obtained.
In one embodiment of the present application, after filtering the document pages with the relevance degree lower than the predetermined threshold value in the first document page set, the document pages with the relevance degree lower than the predetermined threshold value may be fed back to a corresponding third party platform, so as to request the third party platform to change the provided document pages.
With continued reference to FIG. 3, in step 380, the document pages in the second set of document pages are pushed to the user.
In one embodiment of the present application, the method shown in FIG. 9 may also be implemented after pushing the document pages in the second set of document pages to the user.
Referring to FIG. 9, a flowchart of a method after pushing document pages in the second set of document pages to a user according to one embodiment of the present application is shown, which may specifically include steps 391 to 392:
step 391, obtaining feedback behavior data of the user for the pushed document page.
Specifically, in the present application, the feedback behavior data of the user for the pushed document page may refer to behavior data of the user when clicking the pushed document page and when operating inside the clicked document page.
Step 392, training the user behavior feature model based on the feedback behavior data of the user.
In this embodiment, the benefit of further training the user behavior feature model through the behavior data fed back by the user is that the behavior information of the user in the user behavior feature model can be enriched, so that the association degree between the determined document page and the user intention is more accurate.
The following further describes a specific application of the technical solution in the embodiment of the present application, taking a pushing scenario of an applet page as an example with reference to fig. 10:
referring to FIG. 10, a flow chart of a method of pushing applet pages according to one embodiment of the present application is shown. Specifically, steps 1001 to 1004 are included:
in step 1001, identifying a user intention through a keyword input by a user and used for searching a applet page, so as to send the user intention to a third party platform.
At step 1002, the third party platform provides a first set of applet pages including at least one applet page to a user according to the user intent.
Step 1003, filtering the applet pages which do not accord with the user intention in the first applet page set based on the user behavior feature model obtained through training of the user behavior data of the historically full-scale user when searching the applet pages, so as to obtain a second applet page set.
It should be noted that, regarding the user behavior feature model, the user behavior feature model may be obtained by first constructing a heterogram network from user behavior data of a historically full-quantity user when searching for applet pages, and then training the heterogram network.
Step 1004, pushing the applet pages in the second applet page set to a user.
In the above-mentioned pushing scenario of the applet page, some erroneous documents may occur in the corresponding documents of the applet page established by the third party platform, which may cause some applet pages that do not actually conform to the user's intention or even deviate from the user's intention to exist in the first applet page set provided by the third party platform for the user according to the user's intention. Therefore, the applet pages which do not accord with the user intention in the first applet page set can be filtered through the user behavior feature model provided by the application.
In the application, the principle of filtering the applet pages which do not accord with the user intention in the first applet page set through the user behavior feature model is that: the user behavior feature model is trained based on user behavior data of a full-scale user in searching the applet pages, and in the user behavior data of the full-scale user in searching the applet pages, some non-acceptable behaviors, such as behaviors of closing the applet pages, are generated for the applet pages which are not in accordance with the user intention and provided by some third-party platforms. Therefore, the user behavior feature model obtained based on the training of the user behavior data of the historically full-scale user when searching for the applet pages has the capability of identifying the applet pages which do not conform to the user intention (for example, by the technical means for determining the degree of association between the user purpose and the document page set forth in the foregoing embodiment of the application), so that the applet pages which do not conform to the user intention in the first applet page set can be filtered through the user behavior feature model.
Furthermore, by constructing the historical user behavior data of the whole user in searching the applet page as the heterogeneous graph network, the user behavior feature model can learn the historical user behavior data more comprehensively, so that the user behavior feature model has stronger capability of identifying the applet page which does not accord with the user intention.
It will be appreciated by those skilled in the art that the filtering of the applet pages of the first applet page set that do not fit the user's intent by the user behavior feature model has the benefit of: the applet pages pushed to the user can be made more accurate.
In a comprehensive view, in the technical scheme provided by the embodiment of the application, firstly, a user behavior feature model is used, then, the association degree between the document pages in the first document page set and the user intention is determined in the first document page set provided by a third party platform according to the user intention through the user behavior feature model, the document pages with the association degree lower than a preset threshold value in the first document page set are filtered, a second document page set is obtained, and finally, the document pages in the second document page set are pushed to the user. The degree of relevance is used for representing the degree that the document pages in the first document page set accord with the intention of the user, and the document pages with the relevance lower than a preset threshold value are filtered, so that the document pages pushed to the user accord with the intention and the requirement of the user, and further the accuracy of pushing the document pages can be improved.
The following describes an embodiment of the apparatus of the present application, which may be used to execute the method for pushing the document page in the foregoing embodiment of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method for pushing the document page described in the present application.
FIG. 11 illustrates a block diagram of a pushing device for document pages according to one embodiment of the present application.
Referring to fig. 11, a pushing device 1100 for a document page according to an embodiment of the present application includes: a receiving unit 1101, a first determining unit 1102, a filtering unit 1103 and a pushing unit 1104.
Wherein the receiving unit 1101 is configured to receive a first set of document pages provided by a third party platform according to a user intention; a first determining unit 1102, configured to determine, according to a user behavior feature model, a degree of association between a document page in the first document page set and the user intention, where the degree of association is used to characterize a degree to which the document page in the first document page set meets the user intention; a filtering unit 1103, configured to filter document pages in the first set of document pages that have the association degree lower than a predetermined threshold value, to obtain a second set of document pages; a pushing unit 1104 is used for pushing the document pages in the second document page set to the user.
In some embodiments of the present application, based on the foregoing solution, the apparatus further includes a first obtaining unit, configured to obtain, before receiving the first set of document pages provided by the third party platform according to the user intention, keywords input by the user for searching the document pages, so as to determine the user intention of the user; and the sending unit is used for sending the user intention to a third party platform so as to request the third party platform to provide the first document page set.
In some embodiments of the present application, based on the foregoing, the apparatus further comprises a second acquisition unit for acquiring a set of behavioral data for the full amount of users over a predetermined time interval of history; a construction unit, configured to construct an heterograph network according to the behavior data set; and the training unit is used for training a machine learning model through the heterogeneous graph network to obtain the user behavior characteristic model.
In some embodiments of the present application, based on the foregoing solution, the second obtaining unit is configured to: a set of behavioral data is obtained for a full amount of users when entering keywords over a historically predetermined time interval, when clicking on a document page, and when operating within the document page.
In some embodiments of the present application, based on the foregoing scheme, the building unit is configured to: determining the operation behaviors of the keywords, the document pages and the total users in the document pages in the behavior data set as heterogeneous graph network nodes; determining the association between keywords, the association between keywords and document pages and the association between document pages and the operation behaviors of the full-scale users in the document pages in the behavior data set as heterogeneous graph network connection edges; and constructing the heterograph network based on the heterograph network node and the heterograph network connecting edge.
In some embodiments of the present application, based on the foregoing solution, the apparatus further includes a second determining unit configured to determine, before the heterogeneous graph network is constructed based on the heterogeneous graph network node and the heterogeneous graph network connection edge, feature vectors of the respective heterogeneous graph network nodes based on contents of keywords in the behavior data set, contents of a document page, and operational behavior features of a full amount of users inside the document page; and determining the length of each connecting edge of the heterogeneous graph network based on the association times between the keywords in the behavior data set, the association times between the keywords and the document pages and the association times between the document pages and the operation behaviors of the whole user in the document pages, wherein the length of each connecting edge is used for measuring the association tightness degree between different heterogeneous graph network nodes.
In some embodiments of the present application, based on the foregoing solution, the apparatus further includes a feedback unit configured to, after filtering the document pages in the first set of document pages that have the relevance below a predetermined threshold, feed back the document pages that have the relevance below the predetermined threshold to a corresponding third party platform, so as to request the third party platform to change the provided document pages.
In some embodiments of the present application, based on the foregoing solution, the second obtaining unit is configured to: after pushing the document pages in the second document page set to the user, acquiring feedback behavior data of the user for the pushed document pages; the training unit is configured to: and training the user behavior characteristic model based on the feedback behavior data of the user.
In some embodiments of the present application, the document page comprises an applet page based on the foregoing scheme.
Fig. 12 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
It should be noted that, the computer system 1200 of the electronic device shown in fig. 12 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 12, the computer system 1200 includes a central processing unit (Central Processing Unit, CPU) 1201 which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1202 or a program loaded from a storage section 1208 into a random access Memory (Random Access Memory, RAM) 1203. In the RAM 1203, various programs and data required for the system operation are also stored. The CPU 1201, ROM 1202, and RAM 1203 are connected to each other through a bus 1204. An Input/Output (I/O) interface 1205 is also connected to bus 1204.
The following components are connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output portion 1207 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and a speaker, etc.; a storage section 1208 including a hard disk or the like; and a communication section 1209 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 1209 performs communication processing via a network such as the internet. The drive 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 1210 so that a computer program read out therefrom is installed into the storage section 1208 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion 1209, and/or installed from the removable media 1211. When executed by a Central Processing Unit (CPU) 1201, performs the various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, in accordance with embodiments of the present application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (9)
1. A method for pushing a document page, the method comprising:
receiving a first set of document pages provided by a third party platform according to user intent;
determining the association degree of the document pages in the first document page set and the user intention through a user behavior characteristic model, wherein the association degree is used for representing the degree of the document pages in the first document page set conforming to the user intention;
filtering the document pages with the association degree lower than a preset threshold value in the first document page set to obtain a second document page set;
Pushing the document pages in the second document page set to a user;
the user behavior characteristic model is obtained through the following steps:
acquiring behavior data sets when a full-quantity user inputs keywords in a preset time interval in history, clicks a document page and operates in the document page;
determining the operation behaviors of the keywords, the document pages and the total users in the document pages in the behavior data set as heterogeneous graph network nodes;
determining the association between keywords, the association between keywords and document pages and the association between document pages and the operation behaviors of the full-scale users in the document pages in the behavior data set as heterogeneous graph network connection edges;
constructing the heterogeneous graph network based on the heterogeneous graph network nodes and the heterogeneous graph network connecting edges;
and training a machine learning model through the heterogeneous graph network to obtain the user behavior characteristic model.
2. The method of claim 1, wherein prior to receiving the first set of document pages provided by the third party platform according to user intent, the method further comprises:
Acquiring keywords input by a user and used for searching a document page so as to determine the user intention of the user;
and sending the user intention to a third party platform to request the third party platform to provide the first document page set.
3. The method of claim 1, wherein prior to constructing the heterogeneous graph network based on the heterogeneous graph network node and the heterogeneous graph network connection edge, the method further comprises:
determining feature vectors of all heterogeneous graph network nodes based on the content of keywords in the behavior data set, the content of the document page and the operation behavior features of the whole user in the document page;
and determining the length of each connecting edge of the heterogeneous graph network based on the association times between the keywords in the behavior data set, the association times between the keywords and the document pages and the association times between the document pages and the operation behaviors of the whole user in the document pages, wherein the length of each connecting edge is used for measuring the association tightness degree between different heterogeneous graph network nodes.
4. The method of claim 1, wherein after filtering document pages in the first set of document pages that have the relevance below a predetermined threshold, the method further comprises:
And feeding back the document pages with the association degree lower than a preset threshold value to the corresponding third party platform so as to request the third party platform to change the provided document pages.
5. The method of claim 1, wherein after pushing the document pages in the second set of document pages to the user, the method further comprises:
acquiring feedback behavior data of a user aiming at a pushed document page;
and training the user behavior characteristic model based on the feedback behavior data of the user.
6. The method of any one of claims 1 to 5, wherein the document page comprises an applet page.
7. A document page pushing apparatus, comprising:
a receiving unit for receiving a first set of document pages provided by a third party platform according to user intention;
a first determining unit, configured to determine, through a user behavior feature model, a degree of association between a document page in the first document page set and the user intention, where the degree of association is used to characterize a degree to which the document page in the first document page set meets the user intention;
a filtering unit, configured to filter document pages in the first document page set, where the association degree of the document pages is lower than a predetermined threshold value, to obtain a second document page set;
The pushing unit is used for pushing the document pages in the second document page set to a user;
a second acquisition unit configured to acquire a behavior data set when a full-quantity user inputs a keyword in a predetermined time interval of history, clicks on a document page, and operates inside the document page;
the construction unit is configured to determine the operation behaviors of the keywords, the document pages and the whole users in the document pages in the behavior data set as heterogeneous graph network nodes; determining the association between keywords, the association between keywords and document pages and the association between document pages and the operation behaviors of the full-scale users in the document pages in the behavior data set as heterogeneous graph network connection edges; constructing the heterogeneous graph network based on the heterogeneous graph network nodes and the heterogeneous graph network connecting edges;
and the training unit is used for training a machine learning model through the heterogeneous graph network to obtain the user behavior characteristic model.
8. A computer readable medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any one of claims 1 to 6.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more computer programs that, when executed by the one or more processors, cause the electronic device to implement the method of any of claims 1-6.
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| CN113032549B (en) * | 2021-05-31 | 2021-09-10 | 北京明略昭辉科技有限公司 | Document sorting method and device, electronic equipment and storage medium |
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