CN111563201A - Content pushing method, device, server and storage medium - Google Patents

Content pushing method, device, server and storage medium Download PDF

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Publication number
CN111563201A
CN111563201A CN202010354131.5A CN202010354131A CN111563201A CN 111563201 A CN111563201 A CN 111563201A CN 202010354131 A CN202010354131 A CN 202010354131A CN 111563201 A CN111563201 A CN 111563201A
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content
candidate push
list
candidate
vector
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张钦
王延夺
杨一帆
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Beijing Sankuai Online Technology Co Ltd
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Beijing Sankuai Online Technology Co Ltd
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Priority to CN202010354131.5A priority Critical patent/CN111563201A/en
Publication of CN111563201A publication Critical patent/CN111563201A/en
Priority to PCT/CN2021/087230 priority patent/WO2021218634A1/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/95Retrieval from the web
    • G06F16/953Querying, e.g. by the use of web search engines
    • G06F16/9535Search customisation based on user profiles and personalisation
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90Details of database functions independent of the retrieved data types
    • G06F16/95Retrieval from the web
    • G06F16/953Querying, e.g. by the use of web search engines
    • G06F16/9537Spatial or temporal dependent retrieval, e.g. spatiotemporal queries

Abstract

The application discloses a content pushing method, a content pushing device, a server and a storage medium, and belongs to the technical field of internet. The method comprises the following steps: acquiring a candidate push list based on a search request of a terminal, wherein the candidate push list comprises at least one candidate push content; acquiring a historical behavior list of a user, wherein the historical behavior list comprises negative influence content; determining a click-through rate of each candidate push content in the candidate push list based on each negative impact content in the historical behavior list and each candidate push content in the candidate push list; determining the push content according to the click rate of each candidate push content in the candidate push list; and sending the push content to the terminal. According to the content push method, the click rate of each candidate push content is determined based on the historical behavior list and the candidate push content list, so that the click rate of the candidate push content is determined more accurately, and the recommendation accuracy of the push content can be improved.

Description

Content pushing method, device, server and storage medium
Technical Field
The embodiment of the application relates to the technical field of internet, in particular to a content pushing method, a content pushing device, a server and a storage medium.
Background
With the continuous development of network technologies, various applications can be installed and run in terminals such as smart phones, tablet computers and other portable devices, and the applications can improve the working, living and entertainment modes of people.
In the related art, there may be many specific services in the application program, for example, there may be a service of pushing recommended content, the server obtains a historical click record of the user, determines a content type preferred by the user according to the historical click record, searches for content related to the type, and pushes the related content as recommended content to a display interface of the terminal for the user to browse.
However, when browsing content, a user may click on a certain content due to a mistake or other reasons, the content may be a content that the user does not like, and the server considers that the content is a content that the user likes, and then pushes a content similar to the content for the user, so that the matching degree between the pushed content and the user's taste is not high, and the accuracy of the recommendation is reduced to some extent.
Disclosure of Invention
The embodiment of the application provides a content pushing method, a content pushing device, a server and a storage medium, which can be used for solving the problems in the related art. The technical scheme is as follows:
in one aspect, an embodiment of the present application provides a content push method, where the method includes:
acquiring a candidate push list based on a search request of a terminal, wherein the candidate push list comprises at least one candidate push content;
acquiring a historical behavior list of a user, wherein the historical behavior list comprises negative influence content;
determining a click-through rate for each candidate push content in the candidate push list based on each negative impact content in the historical behavior list and each candidate push content in the candidate push list;
determining the push content according to the click rate of each candidate push content in the candidate push list;
and sending the push content to the terminal.
In one possible implementation, the determining the click through rate of each candidate push content in the candidate push list based on each negative influencing content in the historical behavior list and each candidate push content in the candidate push list includes:
determining a vector corresponding to each negative influence content in the historical behavior list to obtain at least one first vector; determining a vector corresponding to each candidate push content in the candidate push list to obtain at least one second vector; based on the at least one first vector and the at least one second vector, a click-through rate for each candidate push content in the candidate push list is determined.
In one possible implementation, the determining a click-through rate of each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector includes:
obtaining a negative interest weight corresponding to each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector; determining a click-through rate of each candidate push content in the candidate push list based on the negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector.
In one possible implementation manner, the determining the click-through rate of each candidate push content in the candidate push list based on the negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector includes:
calculating a weighted average value between the negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector, and taking the weighted average value as the negative interest vector corresponding to each candidate push content in the candidate push list; and determining the click rate of the corresponding candidate push contents according to the negative interest vector corresponding to each candidate push content in the candidate push list.
In a possible implementation manner, the determining the push content according to the click-through rate of each candidate push content in the candidate push list includes:
sorting the candidate push contents in the candidate push list according to the click rate of each candidate push content in the candidate push list;
and selecting a reference number of candidate push contents from the candidate push list according to the sorting result to be used as the push contents.
In one possible implementation, the negative impact content includes at least one of short dwell content and first exposure un-clicked content;
the short-time staying content is the content of which the user browsing time is less than the target browsing time;
the first-position exposed non-clicked content is the content which is arranged at the first position in the history push content and is not clicked.
In another aspect, a content push apparatus is provided, the apparatus including:
the terminal comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a candidate push list based on a search request of the terminal, and the candidate push list comprises at least one candidate push content;
the second acquisition module is used for acquiring a historical behavior list of the user, and the historical behavior list comprises negative influence content;
a first determining module, configured to determine a click through rate of each candidate push content in the candidate push list based on each negative impact content in the historical behavior list and each candidate push content in the candidate push list;
the second determining module is used for determining the push content according to the click rate of each candidate push content in the candidate push list;
and the sending module is used for sending the push content to the terminal.
In a possible implementation manner, the first determining module is configured to determine a vector corresponding to each negative influence content in the historical behavior list, and obtain at least one first vector; determining a vector corresponding to each candidate push content in the candidate push list to obtain at least one second vector; based on the at least one first vector and the at least one second vector, a click-through rate for each candidate push content in the candidate push list is determined.
In a possible implementation manner, the first determining module is configured to obtain a negative interest weight corresponding to each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector; determining a click-through rate of each candidate push content in the candidate push list based on the negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector.
In a possible implementation manner, the first determining module is configured to calculate a weighted average between a negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector, and use the weighted average as a negative interest vector corresponding to each candidate push content in the candidate push list; and determining the click rate of the corresponding candidate push contents according to the negative interest vector corresponding to each candidate push content in the candidate push list.
In a possible implementation manner, the second determining module is configured to rank the candidate push contents in the candidate push list according to a click rate of each candidate push content in the candidate push list; and selecting a reference number of candidate push contents from the candidate push list according to the sorting result to be used as the push contents.
In one possible implementation, the negative impact content includes at least one of short dwell content and first exposure un-clicked content; the short-time staying content is the content of which the user browsing time is less than the target browsing time; the first-position exposed non-clicked content is the content which is arranged at the first position in the history push content and is not clicked.
In another aspect, a server is provided, including:
one or more processors;
one or more memories for storing the one or more processor-executable instructions;
wherein the one or more processors are configured to execute the instructions to implement the content push method provided by the first aspect or any of the possible implementations of the first aspect.
In another aspect, a storage medium is provided, in which instructions, when executed by a processor of a server, enable the server to perform the content push method provided by the first aspect or any one of the possible implementations of the first aspect.
In another aspect, a computer program product is provided, comprising: the computer program product stores at least one instruction that is loaded and executed by a processor to perform the operations described above for content push.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
in the embodiment provided by the application, the click rate of each candidate push content in the candidate push content list is determined according to each negative influence content in the historical behavior list and each candidate push content in the candidate push content list, so that the click rate of each candidate push content is determined more accurately, and the recommendation accuracy of the push content can be increased.
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
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an implementation environment of a content push method provided by an embodiment of the present application;
fig. 2 is a flowchart of a content pushing method provided in an embodiment of the present application;
FIG. 3 is a diagram of a DIN model provided in an embodiment of the present application;
fig. 4 is a flowchart of a content pushing method provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a content pushing apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a server provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an implementation environment provided in an embodiment of the present application, and referring to fig. 1, the implementation environment includes: a terminal 101 and a server 102.
The terminal 101 is connected to the server 102 through a wireless network or a wired network. The terminal 101 may be at least one of a smart phone, a game console, a desktop computer, a tablet computer, an MP3(Moving Picture Experts Group Audio Layer iii, motion Picture Experts compression standard Audio Layer 3) player, an MP4(Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4) player, and a laptop computer. An application client supporting content browsing may be installed and operated in the terminal 101, and the client may be any one of a social application client and a casual shopping client, and of course, the client may also be other types of clients, and the type of the client is not limited in the embodiment of the present application. The terminal 101 may generate a search request in response to a search operation by the user, and the terminal 101 may also transmit the search request to the server 102.
The server 102 may be one server or a server cluster composed of a plurality of servers. The server 102 may also be at least one of a cloud computing platform and a virtualization center, which is not limited in this embodiment of the present application. The server 102 may obtain the candidate push list based on the search request of the terminal. The server 102 may also obtain a list of historical behaviors of the user. The server 102 may determine a click-through rate for each candidate push content in the candidate push list based on the content in the historical behavior list and the content in the candidate push list. Server 102 may also determine the push content based on the click-through rate of the candidate push content. The server 102 may also send the push content to the terminal 101 for display by the terminal 101. Of course, the server 102 may also include other functional servers to provide more comprehensive and diversified services.
The terminal 101 may be generally referred to as one of a plurality of terminals, and the embodiment is only illustrated by the terminal 101. Those skilled in the art will appreciate that the number of terminals 101 described above may be greater or fewer. For example, the number of the terminals 101 may be only a few, or the number of the terminals 101 may be tens or hundreds, or may be more, and the number of the terminals 101 and the type of the device are not limited in the embodiment of the present application.
Based on the foregoing implementation environment, the embodiment of the present application provides a content pushing method, which may be executed by the server 102 in fig. 1, taking the flowchart of the content pushing method provided in the embodiment of the present application shown in fig. 2 as an example. As shown in fig. 2, the method comprises the steps of:
in step 201, a candidate push list is obtained based on a search request of a terminal, where the candidate push list includes at least one candidate push content.
In the embodiment of the application, an application client supporting content browsing is installed and operated in the terminal, and a user can input the name of content to be queried in the client and click a search button. The terminal responds to the search operation of the user to generate a search request, the search request may carry a user identifier of the user, the user identifier may be account information of the user or other information of the user, as long as the user identifier can be used for identifying the user, and the embodiment of the application does not limit the user identifier. The terminal transmits the search request to the server. The server obtains a candidate push list corresponding to a search request based on the search request of the terminal, wherein the candidate push list comprises at least one candidate push content.
For example, the client application is a takeout application, the user inputs "pull" in the client, clicks a search button, and the terminal responds to the click operation of the user to generate a search request, where the search request carries a user identifier of the user, and the user identifier is an account of the user. The terminal sends the search request to the server, and the server obtains a candidate push list based on the search request, wherein the candidate push list comprises at least one family shop.
In step 202, a historical behavior list of the user is obtained, the historical behavior list including negatively affected content.
In an exemplary embodiment, the negative impact content includes at least one of short dwell content and first exposure un-clicked content. The short-time stay content is the content with the browsing time of the user being less than the target browsing time, and the first-position exposed non-clicked content is the content which is arranged at the first position in the history push content and is not clicked. The determination of the first-exposure un-clicked content requires the user to slide down on the terminal page. For example, if the first content is ranked first in the history push content, the user does not click on the first content, and the user does not slide down the page, the first content ranked first cannot expose the clicked content as the first position. The second content is ranked first in the history push content, the user does not click on the second content, but the user slides down the page, and the second content ranked first can be used as the first exposure non-clicked content.
It should be noted that the target browsing time may be set based on experience, may also be adjusted according to an application scenario, and may also be manually set by a user.
In a possible implementation manner, the server may allocate a first storage space to each user, where the first storage space is used to store a user identifier of the user and a historical behavior list, and the historical behavior list includes negative influence content corresponding to the user. And after receiving the search request sent by the terminal, the server analyzes the search request to obtain the user identifier of the user. And searching in the storage space based on the user identification, so that the first storage space corresponding to the user identification can be obtained. And extracting a historical behavior list corresponding to the user identifier in the first storage space, namely acquiring the historical behavior list of the user, so that negative influence content corresponding to the user can be acquired.
In one possible implementation, the determination process of the server on the content with the negative influence may be as follows: the server can count the negative interest characteristics of the user based on an RFM (Recency, Frequency, money, last transaction, transaction Frequency, transaction amount) model so as to determine the negative influence content. Wherein R represents the time interval from the latest transaction time to the current time of the user, and the larger R is, the longer the transaction is not performed by the user. F represents the transaction number of the user in the recent period of time, and the larger F represents the more frequent transaction of the user. M represents the transaction amount of each time of the user, and can be the latest transaction amount or the past average transaction amount. The RFM model can be used for better recording the transaction records of the user in the recent period of time. The time length of the latest period of time can be set according to the transaction type or manually set by the user, and the time length of the latest period of time is not limited in the embodiment of the application. The server can also calculate the RFM value of the historical pushed content on the basis of the RFM model offline statistics of the historical behavior type, the price of the historical behavior and the time interval from the historical behavior to the current time of the user. Based on the RFM value of the historical pushed content, the long-term stable negative interest points of the user can be reliably reflected, and the content corresponding to the negative interest points is also the negative influence content.
In step 203, a click-through rate of each candidate push content in the candidate push list is determined based on each negative influencing content in the historical behavior list and each candidate push content in the candidate push list.
In an embodiment of the present application, based on each negative impact content in the historical behavior list and each candidate push content in the candidate push list, determining the click through rate of each candidate push content in the candidate push list may include the following steps:
step 2031, determining a vector corresponding to each negative influence content in the historical behavior list to obtain at least one first vector.
In one possible implementation, a vector corresponding to the negatively influencing content is stored in the server. The server obtains a vector corresponding to each negative influence content in the historical behavior list from a storage space of the user based on the historical behavior list of the user, and obtains at least one first vector.
For example, if the user's historical behavior list includes 2 negative influence contents, the first vectors corresponding to the 2 negative influence contents may be extracted to obtain 2 first vectors.
Step 2032, determining a vector corresponding to each candidate push content in the candidate push list to obtain at least one second vector;
in a possible implementation manner, a vector corresponding to each candidate push content is stored in the server. The server obtains a vector corresponding to each candidate push content in the candidate push list from a storage space of the user based on the candidate push list of the user to obtain at least one second vector.
For example, if the candidate push list of the user includes 5 candidate push contents, the second vectors corresponding to the 5 candidate push contents may be extracted to obtain 5 second vectors.
Step 2033, determining the click through rate of each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector.
In one possible implementation, the determining the click-through rate of each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector may include:
step 1, obtaining a negative interest weight corresponding to each candidate push content in a candidate push list based on at least one first vector and at least one second vector;
in a possible implementation manner, based on the at least one first vector obtained in step 2031 and the at least one second vector obtained in step 2032, a negative interest weight corresponding to each candidate pushed content in the candidate pushed list is calculated.
Illustratively, the historical behavior list includes 2 pieces of negative impact content, respectively a first negative impact content and a second negative impact content. A first vector corresponding to the first negatively affected content is determined and a second first vector corresponding to the second negatively affected content is determined. The candidate push list includes 5 candidate push contents, which are respectively a first candidate push content, a second candidate push content, a third candidate push content, a fourth candidate push content, and a fifth candidate push content. Determining a first second vector corresponding to the first candidate push content, determining a second vector corresponding to the second candidate push content, determining a third second vector corresponding to the third candidate push content, determining a fourth second vector corresponding to the fourth candidate push content, and determining a fifth second vector corresponding to the fifth candidate push content.
A calculation process of a negative interest weight corresponding to a first candidate push content in the candidate push list is taken as an example for explanation. And calculating an inner product between a first second vector corresponding to the first candidate push content and a first vector corresponding to the first negative influence content in the historical behavior list to obtain a first negative interest weight. And calculating an inner product between a first second vector corresponding to the first candidate push content and a second first vector corresponding to the second negative influence content in the historical behavior list to obtain a second negative interest weight. The two negative interest weights are both negative interest weights corresponding to the first candidate push content.
In a possible implementation manner, the server may further construct the first sequence according to dimensions of a user's interest point, a location, a user's historical behavior, and the like. The first sequence of the structure comprises the user interest points, the area and the historical behavior list of the user. And constructing a second sequence according to the candidate push list, wherein the constructed second sequence comprises candidate push contents searched according to the interest points, the regions and the categories of the historical behaviors of the user. The elements in the first sequence and the second sequence may be vectorized based on the deep network, and a negative interest weight of each candidate push content may be obtained based on the candidate push content in the second sequence and the content in the first sequence.
It should be noted that, the calculation process of the negative interest weight of each candidate push content in the candidate push list is consistent with the calculation process of the negative interest weight of the first candidate push content, and details are not repeated here.
And 2, determining the click rate of each candidate push content in the candidate push list based on the negative interest weight corresponding to each candidate push content in the candidate push list and at least one first vector.
In one possible implementation manner, the step of determining the click through rate of each candidate push content in the candidate push list based on the negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector includes the following steps:
the method comprises the steps of firstly, calculating a weighted average value between a negative interest weight corresponding to each candidate push content in a candidate push list and at least one first vector, and taking the weighted average value as a negative interest vector corresponding to each candidate push content in the candidate push list.
In one possible implementation, the negative interest vector corresponding to each candidate push content in the candidate push list may be determined in any one of the following implementations:
in a first implementation manner, based on the negative interest weight of each candidate push content in the candidate push list obtained in step 1, a weighted average value between the negative interest weight corresponding to each candidate push content and the at least one first vector obtained in step 2031 is calculated, and the weighted average value is used as the negative interest vector corresponding to the candidate push content.
A calculation process of a negative interest vector corresponding to a first candidate push content in the candidate push list is taken as an example for explanation. The first candidate push content has a first negative interest weight and a second negative interest weight. Obtaining a negative interest vector corresponding to the first candidate push content based on the first negative interest weight, the second negative interest weight, the first vector and the second first vector.
For example, the first negative interest weight is multiplied by a first vector to obtain a vector, and the second negative interest weight is multiplied by a second first vector to obtain a vector. And correspondingly adding the two obtained vectors to obtain a negative interest vector corresponding to the first candidate push content.
It should be noted that, the calculation process of the negative interest vector of each candidate push content in the candidate push list is consistent with the calculation process of the negative interest vector of the first candidate push content, and is not described herein again.
In the second implementation manner, the server may further calculate a negative Interest vector of each candidate push content in the candidate push list by using a DIN (Deep Interest Network) model. Fig. 3 below is a schematic diagram of the DIN model shown in the embodiment of the present application. In this fig. 3, a negative interest vector of a candidate push content is calculated based on the vector of the candidate push content and the vector of the content that has an influence on the history behavior list. The DIN model is able to truly reveal the user's desire to click on candidate push content in the candidate push list based on each of the negatively affected content in the user's historical behavior list. The behavior data in the DIN model includes two structures: content diversity and local activation. The content diversity of the behavior data reflects different interest points of the user, and the user clicks on a certain content often because the content touches part of the interest points of the user. An Attention Unit (Attention) mechanism is added to an input layer of the DIN model, so that negative interest vectors of each candidate push content in the candidate push list are determined according to negative influence content in the historical behavior list. The Attention Unit includes FCs, which are field bus controls, and Concat, which is used to connect negatively affected content in the historical behavior list with candidate push content in the candidate push list.
In one possible implementation, the negative interest vector of each candidate push content in the candidate push list is calculated according to the negative interest weight of each candidate push content in the candidate push list and the vector of each negative influence content in the historical behavior list, that is, the negative interest vector of each candidate push content is obtained by the weighting pooling layer based on the negative interest weight of each candidate push content in the candidate push list and the vector of each negative influence content in the historical behavior list. The expression formula of the negative interest vector of each candidate push content in the candidate push list obtained through the weighting pooling layer is as follows:
Figure BDA0002472885980000111
in the above formula, N is the number of negatively affected contents, ViVector, V, representing polar influence content i in a historical behavior listaIs the negative interest vector, g (V), of the a-th candidate push content in the candidate push listi,Va) It is shown that the inner product of the vectors of the polarity-influencing content i and the candidate push content in the history behavior list, i.e. the negative interest weight of the candidate push content, also corresponds to W in the above formulai
It should be noted that the server may select any one of the above implementation manners to determine the negative interest vector of each candidate push content in the candidate push list, which is not limited in this embodiment of the present application.
And secondly, determining the click rate of the corresponding candidate push contents according to the negative interest vector corresponding to each candidate push content in the candidate push list.
In a possible implementation manner, the server calculates the click rate of the candidate pushed content based on the negative interest vector of the candidate pushed content obtained in the first step.
E.g., the negative interest vector of the first candidate push content is (2, 1, 2), the modulo length L of the negative interest vector is calculated,
Figure BDA0002472885980000112
the length of the negative interest vector is used as the click rate of the candidate push content, that is, the click rate of the first candidate push content is 3.
In a possible implementation manner, in order to make the calculation of the click through rate of the candidate push content more accurate, the click through rate of the candidate push content may be further calculated according to the negative interest vector and the positive interest vector of the candidate push content. Wherein, the calculation process of the negative interest vector of the candidate push content is the process of the first step. The calculation process of the forward interest vector of the candidate push content is as follows:
in a possible implementation manner, the historical behavior list of the user may further include positive influence content, where the positive influence content may be historical click content of the user, and a vector corresponding to the positive influence content is determined, so as to obtain at least one third vector. And obtaining a forward interest weight corresponding to each candidate push content in the candidate push list based on the at least one third vector and the at least one second vector. And calculating a weighted average value between the forward interest weight corresponding to each candidate push content in the candidate push list and at least one third vector, and taking the weighted average value as the forward interest vector corresponding to each candidate push content in the candidate push list.
The process of obtaining the positive interest weight corresponding to each candidate pushed content in the candidate pushed list based on the at least one third vector and the at least one second vector is consistent with the process of calculating the negative interest weight corresponding to the candidate pushed content in step 1, and is not described herein again. Calculating a weighted average value between the positive interest weight corresponding to each candidate push content in the candidate push list and the at least one third vector, and using the weighted average value as a positive interest vector corresponding to each candidate push content in the candidate push list in a process consistent with the calculation process of the negative interest vector of the candidate push content in the first step, which is not repeated herein.
In a possible implementation manner, weighted average calculation is performed on the negative interest vector and the positive interest vector of the candidate pushed content to obtain the interest vector of the candidate pushed content, and the click rate of the candidate pushed content is determined based on the interest vector of the candidate pushed content.
For example, the negative interest vector of the first candidate push content is (2, 1, 2), the positive interest vector is (1, 2, 1), the interest vector (2, 2, 2) of the first candidate push content is obtained based on the negative interest vector and the positive interest vector, the modulo length L of the interest vector is calculated,
Figure BDA0002472885980000121
i.e. the click-through rate of the first candidate push content is
Figure BDA0002472885980000122
It should be noted that, if the click through rate of the first candidate push content in the candidate push list is calculated based on the negative interest vector, the click through rates of other candidate push contents in the candidate push list are also calculated based on the negative interest vector. If the click through rate of the first candidate push content in the candidate push list is calculated based on the negative interest vector and the positive interest vector, the click through rates of other candidate push contents in the candidate push list are calculated based on the negative interest vector and the positive interest vector.
In step 204, the push content is determined according to the click rate of each candidate push content in the candidate push list.
In the embodiment of the application, since the number of push contents displayed on the interface of the client installed and running in the terminal is limited, a target number of push contents need to be determined from the candidate push contents. The determination method of the push content may be any one of the following methods:
in the first implementation manner, the pushed content is determined based on the click rate of each candidate pushed content in the candidate pushed list.
Sorting the candidate push contents in the candidate push list according to the click rate of each candidate push content in the candidate push list; and selecting a reference number of candidate push contents from the candidate push list as the push contents according to the sorting result.
For example, there are 5 candidate push contents in the candidate push list, the click rate of the first candidate push content is 0.83, the click rate of the second candidate push content is 0.95, the click rate of the third candidate push content is 0.80, the click rate of the fourth candidate push content is 0.90, the click rate of the fifth candidate push content is 0.88, and the candidate push contents are sorted according to the click rate of the candidate push contents, so that the obtained sorting results are the second candidate push content, the fourth candidate push content, the fifth candidate push content, the first candidate push content, and the third candidate push content. And determining candidate push contents ranked in the top three positions in the ranking result as push contents, namely determining second candidate push contents, fourth candidate push contents and fifth candidate push contents as push contents.
It should be noted that the reference number may be set based on experience, may also be adjusted according to different clients, and may also be set manually by a user.
And determining candidate push contents with the click rate exceeding the target click rate in the candidate push list as the push contents.
The server may set a target click rate, screen candidate push contents having a click rate higher than the target click rate in the candidate push list, and determine the screened candidate push contents as push contents.
For example, there are 5 candidate push contents in the candidate push list, the click-through rate of the first candidate push content is 0.83, the click-through rate of the second candidate push content is 0.95, the click-through rate of the third candidate push content is 0.80, the click-through rate of the fourth candidate push content is 0.90, and the click-through rate of the fifth candidate push content is 0.88. The target click rate was 0.85. And determining candidate push contents with the click rate higher than the target click rate as the push contents, namely determining second candidate push contents, fourth candidate push contents and fifth candidate push contents as the push contents.
It should be noted that the target click rate may be set based on experience, may also be adjusted according to different clients, and may also be manually set by a user.
In step 205, the push content is sent to the terminal.
In the embodiment of the application, the server sends the push content to the terminal corresponding to the user identifier according to the user identifier of the user, and the client installed in the terminal displays the push content.
In the embodiment provided by the application, the click rate of each candidate push content in the candidate push content list is determined according to each negative influence content in the historical behavior list and each candidate push content in the candidate push content list, so that the click rate of each candidate push content is determined more accurately, and the recommendation accuracy of the push content can be increased.
Referring to fig. 4, fig. 4 is a flowchart of a content pushing method provided in an embodiment of the present application, and taking the flowchart of the content pushing method provided in the embodiment of the present application shown in fig. 4 as an example, the method may be illustrated by interaction between the terminal 101 and the server 102 in fig. 1. As shown in fig. 4, the method comprises the steps of:
in step 401, the terminal transmits a search request to the server.
In the embodiment of the present application, an application client supporting content browsing is installed and operated in a terminal, and the client may be a social application client or a casual shopping client. The user can browse the content on the client installed on the terminal device. The user can input the name of the content that the user wants to search in the search box of the client, click the search button, and the terminal responds to the search operation of the user to generate a search request, wherein the search request carries the user identifier of the user, and the user identifier can be the account information of the user or other information of the user, as long as the user identifier can be used for identifying the user, and the user identifier is not limited in the embodiment of the application.
In a possible implementation manner, after the terminal acquires the search request, the terminal may directly send the search request to the server.
In step 402, the server receives a search request sent by the terminal, and obtains a candidate push list based on the search request, where the candidate push list includes at least one candidate push content.
In step 402, the method for obtaining the candidate push list is the same as the method in step 201, and is not described herein again.
In step 403, the server obtains a historical behavior list of the user, the historical behavior list including negative impact content.
In step 403, the method for obtaining the historical behavior list of the user is the same as the method in step 202, and is not described herein again.
In step 404, the server determines a click through rate for each candidate push content in the candidate push list based on each negative influencing content in the historical behavior list and each candidate push content in the candidate push list.
In step 404, the method for determining the click through rate of each candidate push content in the candidate push list is consistent with the method in step 203, and is not repeated herein.
In step 405, the server determines the push content according to the click rate of each candidate push content in the candidate push list.
In step 405, the method for determining the push content is the same as the method in step 204, and is not described herein again.
In step 406, the server sends the push content to the terminal.
In step 406, the process of sending the push content to the terminal by the server is the same as the process in step 205, and is not described herein again.
In step 407, the terminal receives the push content transmitted from the server and displays the push content.
In step 407, after receiving the push content sent by the server, the terminal displays the push content on an interface of a client installed on the terminal, so that the user can browse and view the push content conveniently.
In the embodiment provided by the application, the click rate of each candidate push content in the candidate push content list is determined according to each negative influence content in the historical behavior list and each candidate push content in the candidate push content list, so that the click rate of each candidate push content is determined more accurately, and the recommendation accuracy of the push content can be increased.
Fig. 5 is a schematic structural diagram of a content pushing apparatus according to an embodiment of the present application, and as shown in fig. 5, the content pushing apparatus includes:
a first obtaining module 501, configured to obtain a candidate push list based on a search request of a terminal, where the candidate push list includes at least one candidate push content;
a second obtaining module 502, configured to obtain a historical behavior list of the user, where the historical behavior list includes negative influence content;
a first determining module 503, configured to determine a click through rate of each candidate push content in the candidate push list based on each negative impact content in the historical behavior list and each candidate push content in the candidate push list;
a second determining module 504, configured to determine push content according to a click rate of each candidate push content in the candidate push list;
a sending module 505, configured to send the push content to the terminal.
In a possible implementation manner, the first determining module 503 is configured to determine a vector corresponding to each negative influence content in the historical behavior list, to obtain at least one first vector; determining a vector corresponding to each candidate push content in the candidate push list to obtain at least one second vector; based on the at least one first vector and the at least one second vector, a click-through rate for each candidate push content in the candidate push list is determined.
In a possible implementation manner, the first determining module 503 is configured to obtain a negative interest weight corresponding to each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector; determining a click-through rate of each candidate push content in the candidate push list based on the negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector.
In a possible implementation manner, the first determining module 503 is configured to calculate a weighted average between a negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector, and use the weighted average as the negative interest vector corresponding to each candidate push content in the candidate push list; and determining the click rate of the corresponding candidate push contents according to the negative interest vector corresponding to each candidate push content in the candidate push list.
In a possible implementation manner, the second determining module 504 is configured to rank the candidate push contents in the candidate push list according to a click rate of each candidate push content in the candidate push list; and selecting a reference number of candidate push contents from the candidate push list according to the sorting result to be used as the push contents.
In one possible implementation, the negative impact content includes at least one of short dwell content and first exposure un-clicked content; the short-time staying content is the content of which the user browsing time is less than the target browsing time; the first-position exposed non-clicked content is the content which is arranged at the first position in the history push content and is not clicked.
The device determines the click rate of each candidate push content in the candidate push list according to each negative influence content in the historical behavior list and each candidate push content in the candidate push content list, so that the click rate of each candidate push content is determined more accurately, and the recommendation accuracy of the push content can be improved.
It should be noted that: in the content push device provided in the foregoing embodiment, when pushing content, only the division of the functional modules is illustrated, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the content push device may be divided into different functional modules to complete all or part of the functions described above. In addition, the content push device and the content push method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
Fig. 6 is a schematic structural diagram of a server 600 according to an embodiment of the present application, where the server 600 may generate a relatively large difference due to a difference in configuration or performance, and may include one or more processors (CPUs) 601 and one or more memories 602, where at least one instruction is stored in the one or more memories 602, and is loaded and executed by the one or more processors 601 to implement the content pushing method according to the foregoing method embodiment. Of course, the server 600 may also have components such as a wired or wireless network interface, a keyboard, and an input/output interface, so as to perform input and output, and the server 600 may also include other components for implementing the functions of the device, which is not described herein again.
Fig. 7 is a schematic structural diagram of a terminal according to an embodiment of the present application. The terminal 700 may be: a smart phone, a tablet computer, an MP3(Moving Picture Experts Group Audio Layer III, motion video Experts compression standard Audio Layer 3) player, an MP4(Moving Picture Experts Group Audio Layer IV, motion video Experts compression standard Audio Layer 4) player, a notebook computer or a desktop computer. Terminal 700 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, and so on.
In general, terminal 700 includes: one or more processors 701 and one or more memories 702.
The processor 701 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 701 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 701 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 701 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 701 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
Memory 702 may include one or more computer-readable storage media, which may be non-transitory. Memory 702 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 702 is used to store at least one instruction for execution by processor 701 to implement a content push method provided by method embodiments herein.
In some embodiments, the terminal 700 may further optionally include: a peripheral interface 703 and at least one peripheral. The processor 701, the memory 702, and the peripheral interface 703 may be connected by buses or signal lines. Various peripheral devices may be connected to peripheral interface 703 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 704, a display screen 705, a camera assembly 706, an audio circuit 707, a positioning component 708, and a power source 709.
The peripheral interface 703 may be used to connect at least one peripheral related to I/O (Input/Output) to the processor 701 and the memory 702. In some embodiments, processor 701, memory 702, and peripheral interface 703 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 701, the memory 702, and the peripheral interface 703 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.
The Radio Frequency circuit 704 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 704 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 704 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 704 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 704 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the radio frequency circuit 704 may also include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display screen 705 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 705 is a touch display screen, the display screen 705 also has the ability to capture touch signals on or over the surface of the display screen 705. The touch signal may be input to the processor 701 as a control signal for processing. At this point, the display 705 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display 705 may be one, providing the front panel of the terminal 700; in other embodiments, the display 705 can be at least two, respectively disposed on different surfaces of the terminal 700 or in a folded design; in some embodiments, the display 705 may be a flexible display disposed on a curved surface or on a folded surface of the terminal 700. Even more, the display 705 may be arranged in a non-rectangular irregular pattern, i.e. a shaped screen. The Display 705 may be made of LCD (liquid crystal Display), OLED (Organic Light-Emitting Diode), or the like.
The camera assembly 706 is used to capture images or video. Optionally, camera assembly 706 includes a front camera and a rear camera. Generally, a front camera is disposed at a front panel of the terminal, and a rear camera is disposed at a rear surface of the terminal. In some embodiments, the number of the rear cameras is at least two, and each rear camera is any one of a main camera, a depth-of-field camera, a wide-angle camera and a telephoto camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions or other fusion shooting functions. In some embodiments, camera assembly 706 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.
The audio circuitry 707 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 701 for processing or inputting the electric signals to the radio frequency circuit 704 to realize voice communication. For the purpose of stereo sound collection or noise reduction, a plurality of microphones may be provided at different portions of the terminal 700. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is used to convert electrical signals from the processor 701 or the radio frequency circuit 704 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, the audio circuitry 707 may also include a headphone jack.
The positioning component 708 is used to locate the current geographic position of the terminal 700 to implement navigation or LBS (location based Service). The positioning component 708 may be a positioning component based on the GPS (global positioning System) in the united states, the beidou System in china, the graves System in russia, or the galileo System in the european union.
Power supply 709 is provided to supply power to various components of terminal 700. The power source 709 may be alternating current, direct current, disposable batteries, or rechargeable batteries. When power source 709 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.
In some embodiments, the terminal 700 can also include one or more sensors 170. The one or more sensors 170 include, but are not limited to: acceleration sensor 711, gyro sensor 712, pressure sensor 711, fingerprint sensor 714, optical sensor 715, and proximity sensor 716.
The acceleration sensor 711 can detect the magnitude of acceleration in three coordinate axes of a coordinate system established with the terminal 700. For example, the acceleration sensor 711 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 701 may control the display screen 705 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 711. The acceleration sensor 711 may also be used for acquisition of motion data of a game or a user.
The gyro sensor 712 may detect a body direction and a rotation angle of the terminal 700, and the gyro sensor 712 may cooperate with the acceleration sensor 711 to acquire a 3D motion of the terminal 700 by the user. From the data collected by the gyro sensor 712, the processor 701 may implement the following functions: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization at the time of photographing, game control, and inertial navigation.
The pressure sensor 711 may be disposed on a side frame of the terminal 700 and/or on a lower layer of the display 705. When the pressure sensor 711 is disposed on a side frame of the terminal 700, a user's holding signal of the terminal 700 may be detected, and the processor 701 performs left-right hand recognition or shortcut operation according to the holding signal collected by the pressure sensor 711. When the pressure sensor 711 is disposed at the lower layer of the display 705, the processor 701 controls the operability control on the UI interface according to the pressure operation of the user on the display 705. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.
The fingerprint sensor 714 is used for collecting a fingerprint of a user, and the processor 701 identifies the identity of the user according to the fingerprint collected by the fingerprint sensor 714, or the fingerprint sensor 714 identifies the identity of the user according to the collected fingerprint. When the user identity is identified as a trusted identity, the processor 701 authorizes the user to perform relevant sensitive operations, including unlocking a screen, viewing encrypted information, downloading software, paying, changing settings, and the like. The fingerprint sensor 714 may be disposed on the front, back, or side of the terminal 700. When a physical button or a vendor Logo is provided on the terminal 700, the fingerprint sensor 714 may be integrated with the physical button or the vendor Logo.
The optical sensor 715 is used to collect the ambient light intensity. In one embodiment, the processor 701 may control the display brightness of the display screen 705 based on the ambient light intensity collected by the optical sensor 715. Specifically, when the ambient light intensity is high, the display brightness of the display screen 705 is increased; when the ambient light intensity is low, the display brightness of the display screen 705 is adjusted down. In another embodiment, processor 701 may also dynamically adjust the shooting parameters of camera assembly 706 based on the ambient light intensity collected by optical sensor 715.
A proximity sensor 716, also referred to as a distance sensor, is typically disposed on a front panel of the terminal 700. The proximity sensor 716 is used to collect the distance between the user and the front surface of the terminal 700. In one embodiment, when the proximity sensor 716 detects that the distance between the user and the front surface of the terminal 700 gradually decreases, the processor 701 controls the display 705 to switch from the bright screen state to the dark screen state; when the proximity sensor 716 detects that the distance between the user and the front surface of the terminal 700 is gradually increased, the processor 701 controls the display 705 to switch from the breath-screen state to the bright-screen state.
Those skilled in the art will appreciate that the configuration shown in fig. 7 is not intended to be limiting of terminal 700 and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.
In an exemplary embodiment, there is also provided a computer-readable storage medium having at least one program code stored therein, the at least one program code being loaded and executed by a processor of a computer device to implement any of the above-mentioned content push methods.
Alternatively, the computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
It should be understood that reference to "a plurality" herein means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The above description is only exemplary of the present application and is not intended to limit the present application, and any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A method for pushing content, the method comprising:
acquiring a candidate push list based on a search request of a terminal, wherein the candidate push list comprises at least one candidate push content;
acquiring a historical behavior list of a user, wherein the historical behavior list comprises negative influence content;
determining a click-through rate for each candidate push content in the candidate push list based on each negative impact content in the historical behavior list and each candidate push content in the candidate push list;
determining the push content according to the click rate of each candidate push content in the candidate push list;
and sending the push content to the terminal.
2. The method of claim 1, wherein determining the click-through rate for each candidate push content in the candidate push list based on each negative influencing content in the historical behavior list and each candidate push content in the candidate push list comprises:
determining a vector corresponding to each negative influence content in the historical behavior list to obtain at least one first vector;
determining a vector corresponding to each candidate push content in the candidate push list to obtain at least one second vector;
determining a click-through rate for each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector.
3. The method of claim 2, wherein determining the click-through rate of each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector comprises:
obtaining a negative interest weight corresponding to each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector;
determining a click-through rate of each candidate push content in the candidate push list based on the negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector.
4. The method of claim 3, wherein determining the click-through rate of each candidate push content in the candidate push list based on the negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector comprises:
calculating a weighted average value between a negative interest weight corresponding to each candidate push content in the candidate push list and the at least one first vector, and taking the weighted average value as a negative interest vector corresponding to each candidate push content in the candidate push list;
and determining the click rate of the corresponding candidate push contents according to the negative interest vector corresponding to each candidate push content in the candidate push list.
5. The method of claim 1, wherein the determining push content according to the click-through rate of each candidate push content in the candidate push list comprises:
sorting the candidate push contents in the candidate push list according to the click rate of each candidate push content in the candidate push list;
and selecting a reference number of candidate push contents from the candidate push list according to the sorting result to be used as the push contents.
6. The method of any of claims 1-5, wherein the negative impact content comprises at least one of short dwell content and first exposure un-clicked content;
the short-time staying content is the content of which the user browsing time is less than the target browsing time;
the first-position exposure un-clicked content is the content which is ranked at the first position in the history pushing content and is not clicked.
7. A content pushing apparatus, characterized in that the apparatus comprises:
the terminal comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a candidate push list based on a search request of the terminal, and the candidate push list comprises at least one candidate push content;
the second acquisition module is used for acquiring a historical behavior list of the user, and the historical behavior list comprises negative influence content;
a first determining module, configured to determine a click through rate of each candidate push content in the candidate push list based on each negative impact content in the historical behavior list and each candidate push content in the candidate push list;
the second determining module is used for determining the push contents according to the click rate of each candidate push content in the candidate push list;
and the sending module is used for sending the push content to the terminal.
8. The apparatus of claim 7, wherein the first determining module is configured to determine a vector corresponding to each negative impact content in the historical behavior list, resulting in at least one first vector; determining a vector corresponding to each candidate push content in the candidate push list to obtain at least one second vector; determining a click-through rate for each candidate push content in the candidate push list based on the at least one first vector and the at least one second vector.
9. A server, characterized in that the server comprises a processor and a memory, wherein at least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor to implement the content push method according to any one of claims 1 to 6.
10. A computer-readable storage medium, having at least one program code stored therein, the at least one program code being loaded and executed by a processor to implement the content push method according to any one of claims 1 to 6.
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WO2021218634A1 (en) * 2020-04-29 2021-11-04 北京三快在线科技有限公司 Content pushing
CN112989198A (en) * 2021-03-30 2021-06-18 北京三快在线科技有限公司 Push content determination method, device, equipment and computer-readable storage medium
CN112989198B (en) * 2021-03-30 2022-06-07 北京三快在线科技有限公司 Push content determination method, device, equipment and computer-readable storage medium

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