CN113395304A

CN113395304A - Information pushing method and system, client, storage medium and computing terminal

Info

Publication number: CN113395304A
Application number: CN202010172531.4A
Authority: CN
Inventors: 李春柏; 夏云; 潘明明
Original assignee: Alibaba Group Holding Ltd
Current assignee: Alibaba Group Holding Ltd
Priority date: 2020-03-12
Filing date: 2020-03-12
Publication date: 2021-09-14

Abstract

The application discloses an information pushing method and system, a client, a storage medium and a computing terminal. Wherein, the method comprises the following steps: the method comprises the steps that a client side obtains behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area; the client adjusts target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in a preset area in the information set; the client displays the adjusted information set. The method and the device solve the technical problem that in the related technology, the client acquires the information set from the server at fixed time, so that the pushing effect is poor.

Description

Information pushing method and system, client, storage medium and computing terminal

Technical Field

The application relates to the field of internet, in particular to an information pushing method and system, a client, a storage medium and a computing terminal.

Background

In an e-commerce shopping scenario, a traditional client push mode is to request a server to present content to a User page by page through a fixed time, as shown in fig. 1, after a terminal detects User input, a cloud is triggered to push the content, the cloud makes a decision based on data backflow, and issues decision data to the terminal, and the terminal presents the decision data through a User Interface (UI).

However, the shopping intention of the user may change at any time during the browsing process, and the pushing system cannot capture the interest points which are passed by the user in time, so that the user can push results more accurately. When the current preference of the user is not matched with the pushed content, the content browsing amount and the click rate of the user are reduced, and finally, the user even leaves the client. The result is that the platform will therefore incur the loss of flow conversion and commodity bargain due in part to perceived hysteresis.

Aiming at the problem that the pushing effect is poor due to the fact that a client acquires an information set from a server at a fixed time in the related art, an effective solution is not provided at present.

Disclosure of Invention

The embodiment of the application provides an information pushing method and system, a client, a storage medium and a computing terminal, so as to at least solve the technical problem that in the related art, the client acquires an information set from a server at a fixed time, so that the pushing effect is poor.

According to an aspect of an embodiment of the present application, there is provided an information pushing method, including: the method comprises the steps that a client side obtains behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area; the client adjusts target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in a preset area in the information set; the client displays the adjusted information set.

According to another aspect of the embodiments of the present application, there is also provided an information pushing method, including: the client displays partial content of the information set in a preset area; after detecting the interactive behavior of the target object in the preset area, the client displays the adjusted information set on the preset area, wherein the adjusted information set is obtained by adjusting the target content in the information set based on the behavior data corresponding to the interactive behavior, and the target content is the content which is not displayed in the preset area in the information set.

According to another aspect of the embodiments of the present application, there is also provided an information push system, including: a server for sending a set of information; the client is communicated with the server and used for acquiring behavior data of the target object in a preset area, adjusting target content in the information set based on the behavior data to obtain an adjusted information set, and displaying the adjusted information set, wherein partial content of the information set is displayed in the preset area, and the target content is content which is not displayed in the preset area in the information set.

According to another aspect of the embodiments of the present application, there is also provided a client, including: a display for displaying a part of the content of the information set in a preset area; the acquisition device is used for acquiring behavior data of the target object in a preset area; and the processor is connected with the display and the acquisition device and used for adjusting the target content in the information set based on the behavior data to obtain an adjusted information set and controlling the display to display the adjusted information set, wherein the target content is the content which is not displayed in the preset area in the information set.

According to another aspect of the embodiments of the present application, there is also provided a storage medium including a stored program, wherein when the program runs, a device on which the storage medium is located is controlled to execute the above-mentioned information push method.

According to another aspect of the embodiments of the present application, there is also provided a computing terminal, including: the information pushing system comprises a memory and a processor, wherein the processor is used for operating a program stored in the memory, and the program executes the information pushing method when running.

According to another aspect of the embodiments of the present application, there is also provided an information push system, including: a processor; and a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: acquiring behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area; adjusting target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in a preset area in the information set; and displaying the adjusted information set.

In the embodiment of the application, after the client acquires the behavior data of the target object in the preset area, the target content in the information set can be adjusted based on the behavior data to obtain the adjusted information set, and the adjusted information set is displayed, so that the purpose of adjusting the information in real time according to the preference of the user is achieved. Compared with the prior art, the client directly performs user intention analysis locally, and timely adjusts the content which is not displayed in the preset area in the information set, so that real-time identification of user intention and real-time updating of push content are realized, millisecond-level real-time response is given to each walking movement of the user by taking user behaviors as trigger points, the recommendation browsing body feeling of the user is improved, the recommendation effect is further improved, the technical effect of greatly reducing resource consumption of the server is achieved, and the technical problem that the push effect is poor due to the fact that the client acquires the information set from the server at fixed time in the related art is solved.

Drawings

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

FIG. 1 is a schematic diagram of a cloud decision scheme according to the prior art;

fig. 2 is a block diagram of a hardware structure of a computer terminal for implementing an information push method according to an embodiment of the present application;

FIG. 3 is a flow chart of a method for pushing information according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a local decision-making scheme according to an embodiment of the present application;

FIG. 5 is a schematic diagram of an alternative intelligent refresh scheme according to an embodiment of the present application;

FIG. 6 is a schematic diagram of another alternative intelligent refresh scheme according to an embodiment of the present application;

FIG. 7 is a schematic diagram of an alternative local reordering scheme in accordance with an embodiment of the present application;

FIG. 8 is a diagram illustrating an alternative content cache pool according to an embodiment of the present application;

FIG. 9 is a schematic diagram of an alternative information push scheme according to an embodiment of the present application;

FIG. 10 is an interaction diagram of an alternative information push method according to an embodiment of the application;

FIG. 11 is a flow chart of another information pushing method according to an embodiment of the application;

FIG. 12 is a schematic diagram of an information-pushing device according to an embodiment of the present application;

FIG. 13 is a schematic diagram of another information-pushing device in accordance with an embodiment of the present application;

FIG. 14 is a schematic diagram of an information push system according to an embodiment of the present application;

FIG. 15 is a schematic diagram of a client according to an embodiment of the present application; and

fig. 16 is a block diagram of a computer terminal according to an embodiment of the present application.

Detailed Description

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

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

First, some terms or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:

and (3) edge calculation: data calculations are made on the side of the device that occurs near the data source.

AI technology: i.e. artificial intelligence, uses a computer to simulate certain mental processes and intelligent behaviors of a human.

Intelligent decision making: AI techniques are used to make decisions, such as deciding to request, deciding to change the order, etc., based on actions or other events that occur to the user.

Information flow: it may refer to a group of information that has a common information source and receiver of information during movement in the same direction in space and time, i.e. the set of all information passed from one information source to another.

Example 1

According to an embodiment of the present application, there is provided an information pushing method, it should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that here.

The method provided by the embodiment of the application can be executed in a mobile terminal, a computer terminal or a similar operation device. Fig. 2 shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing the information push method. As shown in fig. 2, the computer terminal 10 (or mobile device 10) may include one or more (shown as 102a, 102b, … …, 102 n) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 2 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 2, or have a different configuration than shown in FIG. 2.

It should be noted that the one or more processors 102 and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistance termination path to interface with).

The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the information pushing method in the embodiment of the present application, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the information pushing method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).

It should be noted here that in some alternative embodiments, the computer device (or mobile device) shown in fig. 2 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 2 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computer device (or mobile device) described above.

It should be noted here that, in some embodiments, the computer device (or mobile device) shown in fig. 2 has a touch display (also referred to as a "touch screen" or "touch display screen"). In some embodiments, the computer device (or mobile device) shown in fig. 1 above has a Graphical User Interface (GUI) with which a user can interact by touching finger contacts and/or gestures on a touch-sensitive surface, where the human interaction functionality optionally includes the following interactions: executable instructions for creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, emailing, call interfacing, playing digital video, playing digital music, and/or web browsing, etc., for performing the above-described human-computer interaction functions, are configured/stored in one or more processor-executable computer program products or readable storage media.

Under the operating environment, the application provides an information pushing method as shown in fig. 3. Fig. 3 is a flowchart of an information pushing method according to an embodiment of the present application. As shown in fig. 3, the method comprises the steps of:

step S302, the client obtains behavior data of the target object in a preset area, where a part of the content of the information set is displayed in the preset area.

The client in the above steps may be an application installed in a computer terminal or a Mobile terminal, and the target object may be a user operating the client, for example, the user may be an owner of the computer terminal or the Mobile terminal, where the Mobile terminal may be a terminal such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), and a PAD, but is not limited thereto.

The information set may be an information stream that is sent by the server to the client in advance and displayed to the user, and the information set may be determined based on an operation of the user. All information in the information set is displayed in sequence, and a user browses detailed contents of the information by operating the client side or switches to browse different information. For example, taking an e-commerce shopping scenario as an example, the client may be an application provided by the e-commerce shopping platform to the user, and the information set may be a set of items determined by the server based on search content input by the user. For another example, taking a live scene as an example, the client may be an application program provided by the live platform to the user, and the information set may be a live room set determined by the server based on search content input by the user.

The preset area can be a display area of the information set on the client, and due to the limitation of the size of the interactive interface, the preset area can only display part of the content in the information set. The content displayed in the preset area is the exposure content in the information set.

In the operation process, the user generally browses the information according to the preference of the user, so the operation behavior of the user reflects the current intention of the user. On this basis, in order to accurately obtain the current intention of the user and push information to the user based on the current intention of the user, the client may detect operation behaviors of the user such as clicking and sliding in real time and record corresponding behavior data, where the behavior data may include: the detailed content of the information browsed by the user, the time length of the information browsed by the user, and the like, but the method is not limited to this, and the recording can be performed according to actual needs.

Step S304, the client adjusts the target content in the information set based on the behavior data to obtain the adjusted information set, wherein the target content is the content which is not displayed in the preset area in the information set.

The target content in the above steps may refer to unexposed content in the information set, that is, content that has not been viewed by the user, and the adjustment may include, but is not limited to, content replacement, reordering, addition, and deletion.

It should be noted that, the interest of the user in the information changes in real time, in order to ensure that the information pushed to the user can meet the current intention of the user to the greatest extent, and the amount of information that can be displayed in the interface of the client is limited, therefore, the target content may be information that is displayed in the interface in the next exposure, and the remaining information that is not yet exposed may continue to wait for adjustment.

Step S306, the client displays the adjusted information set.

The displaying in the above step may be displaying the adjusted information set in an interface of the client.

In an alternative embodiment, as shown in fig. 4, the decision may be performed in a pre-stage manner, and the decision is directly performed at the terminal side, and the cloud (server) only performs data transmission. The client captures user behaviors of the user in a display area of the information set, analyzes the current intention of the user based on behavior data, further adjusts unexposed contents in the information set based on an analysis result, adjusts the contents which are interested by the user to the front, and adjusts the contents which are not interested by the user to the back, or even discards the contents.

Based on the scheme provided by the embodiment of the application, after the client acquires the behavior data of the target object in the preset area, the client can adjust the target content in the information set based on the behavior data to obtain the adjusted information set, and display the adjusted information set, so that the purpose of adjusting the information in real time according to the preference of the user is achieved. Compared with the prior art, the client directly performs user intention analysis locally, and timely adjusts the content which is not displayed in the preset area in the information set, so that real-time identification of user intention and real-time updating of push content are realized, millisecond-level real-time response is given to each walking movement of the user by taking user behaviors as trigger points, the recommendation browsing body feeling of the user is improved, the recommendation effect is further improved, the technical effect of greatly reducing resource consumption of the server is achieved, and the technical problem that the push effect is poor due to the fact that the client acquires the information set from the server at fixed time in the related art is solved.

In the above embodiment of the present application, adjusting target content in an information set based on behavior data to obtain an adjusted information set includes: acquiring feature data corresponding to a target object stored in a server, wherein the feature data comprises: the historical characteristics of the target object, and the characteristics of the historical content browsed by the target object; processing the behavior data and the characteristic data by using the trained processing model to obtain the current characteristic of the target object; and adjusting the target content based on the current characteristics to obtain an adjusted information set.

In the e-commerce shopping scenario, the characteristic data may be user characteristics and commodity characteristics obtained by the server through long-term analysis based on the behavior data of the user, and the user characteristics may include, but are not limited to, the gender, age, purchasing power and the like of the user, and the commodity characteristics may include, but are not limited to, the type, price, quality and the like of the commodity, and are stored in the database of the server. In a live broadcast scene, the feature data may be user features and live broadcast room features obtained by the server based on the behavior data analysis of the user for a long time, and are stored in a database of the server, the user features may include, but are not limited to, the gender, age, and the like of the user, and the live broadcast room features may include, but are not limited to, the type of the live broadcast room, the live broadcast time, the anchor information, and the like.

The processing model may be a model trained based on historical data, and may include, but is not limited to, a neural network model, and a higher-precision processing model may be obtained by training through a large amount of historical data. The current feature may be a feature capable of representing a current intention of the user, and this is not particularly limited in this application, and may be determined according to actual needs.

In an alternative embodiment, the client may obtain the feature data from the server, and further input the obtained feature data and the locally stored behavior data into a trained processing model, where the output of the model is also the current feature of the user, and the feature may represent the current intention of the user. After analyzing the current intention of the user, unexposed contents in the information set can be changed at any time according to the preference of the user, and the preference of the user is matched through adding, deleting, sorting, content transformation and the like.

In the above embodiment of the present application, adjusting the target content based on the current feature to obtain an adjusted information set includes: determining whether to send a request to a server based on the current characteristics, the target content, and second content stored in the local cache; if the request is sent to the server, obtaining an adjusted information set based on the target content, the second content and the response returned by the server; if it is determined not to send a request to the server, an adjusted set of information is derived based on the current characteristics, the target content, and the second content.

The local cache may be a content cache pool locally deployed by the client, and the content cache pool may store content not included in the adjusted information set, so as to prevent the client from frequently requesting the server to obtain the content. For example, in an e-commerce shopping scenario, the local cache may be a pool of item caches. For another example, in a live scenario, the local cache may be a live room cache pool.

In the process of browsing the information set, the interest point of the user may change, or even may change greatly, and under the condition that the interest point of the user changes greatly, the unexposed content in the information set and the content stored in the local cache may not accord with the preference of the user, at this time, the client may access the server, and obtain the content which accords with the preference of the user better from the server. In addition, in order to ensure that the adjusted information set better conforms to the preference of the user, the client can access the server, the server analyzes the adjustment rule of the information set, and the client further adjusts the information set locally based on the adjustment rule.

Optionally, the request may include: current characteristics or behavioral data that satisfy a first condition. In order to ensure that the client can acquire the content which better meets the user preference from the server, the client can directly upload the locally analyzed current characteristics to the server, and the server analyzes and issues the current characteristics, or can directly upload the behavior data meeting the first condition to the server. The first condition may be a fixed rule stored in the client, for example, the first condition may be a time rule, that is, behavior data in a period of time is uploaded to the server, but not all behavior data is uploaded.

In an alternative embodiment, the adjustment of the information set may be divided into two manners, namely, a local rearrangement manner and an intelligent refresh manner, and for the scheme of the local rearrangement, the client may directly adjust the unexposed content based on the analyzed user intention, for example, directly reorder or delete the unexposed content, or add or replace the unexposed content with the content stored in the local cache, and the like. For the intelligent refresh scheme, the client may send a request to the server and receive data sent by the server, and further adjust the unexposed content based on the analyzed user intention and the data sent by the server, for example, add or replace the unexposed content by using the content stored in the local cache and the data sent by the server, and reorder the unexposed content.

The following two schemes are described in detail respectively:

in the above embodiment of the present application, the adjusted information set is obtained based on the target content, the second content, and the response returned by the server, and includes one of the following: processing the current characteristics, the target content, the second content and the third content by using the trained first adjustment model to obtain an adjusted information set; and processing the target content and the second content by using the adjustment rule to obtain an adjusted information set.

Optionally, the response may include one of: and the third content or an adjustment rule, wherein the third content may be content screened by the server and better conforming to the preference of the user, the adjustment rule may be a sorting rule for re-sorting the unexposed content, and the client performs primary local sorting based on the sorting rule issued by the server.

The first adjustment model in the above steps may be a model trained based on historical data, and may include, but is not limited to, a neural network model, and the first adjustment model with higher accuracy may be obtained by training through a large amount of historical data.

In an optional embodiment, the intelligent refresh scheme may be specifically divided into two cases, for the first case, the client may locally analyze whether the unexposed content meets the preference of the user, if not, the client needs to obtain the content better meeting the preference of the user from the server, after obtaining the data returned by the server, the client may locally input the current feature and all the content into the first adjustment model, and the output of the model is the adjusted information set. For the second case, the client may directly determine whether the information set needs to be adjusted after analyzing the user preference, request the server to obtain the adjustment rule if the adjustment needs to be performed, and locally adjust the information set based on the adjustment rule after obtaining the data returned by the server.

A preferred intelligent refresh scheme of the present application is described in detail below with reference to fig. 5 and 6, wherein fig. 5 shows the information flow before the non-adjustment and fig. 6 shows the information flow after the adjustment. The unexposed content before adjustment is from top to bottom, and from left to right: content 0, content 1, content 2, and content 3. After detecting user behaviors, for example, a user clicks and browses a first information item, the client may collect the user behaviors and store the user behaviors in a user data center, further input the user behaviors and unexposed contents into an intelligent refresh model, analyze the user intention, after analyzing the user intention, request a server, and obtain a preferred commodity issued by the server, adjust the unexposed contents based on the preferred commodity, wherein the adjusted unexposed contents are, from top to bottom, from left to right: content 4, content 5, content 6, and content 7.

In the above embodiment of the present application, obtaining an adjusted information set based on the current feature, the target content, and the second content includes: and processing the current characteristics, the target content and the second content by using the trained second adjustment model to obtain an adjusted information set.

The second adjustment model in the above steps may be a model obtained by training based on historical data, and may include, but is not limited to, a neural network model, and the second adjustment model with higher accuracy may be obtained by training through a large amount of historical data.

In an alternative embodiment, for the local rearrangement scheme, the client locally may directly input the current features and all the contents into the second adjustment model, and the output of the model is the adjusted information set.

A preferred local reordering scheme of the present application is described in detail below with reference to fig. 7. As shown in fig. 7, the unexposed content before adjustment is, from top to bottom, respectively: content 0, content 1, content 2, and content 3. After detecting user behaviors, for example, after a user clicks and browses a first information item, the client may collect the user behaviors and store the user behaviors in a user data center, further input the user behaviors into a rearrangement model for user intention analysis, and after analyzing the user intentions, may make a local decision and adjust information flow, where the adjusted unexposed content is, from top to bottom, from left to right: content 3, content 2, content 0, and content 1.

It should be noted that the local reordering scheme may also be performed in other non-model manners, such as manually performing local ordering according to rules, for example, directly ordering according to types.

In the above embodiments of the present application, determining whether to send a request to the server based on the current feature, the target content, and the second content stored in the local cache includes: and processing the current characteristics, the target content and the second content by using the trained determination model to determine whether to send a request to the server.

The determination model in the above steps may be a model trained based on historical data, and may include, but is not limited to, a neural network model, and a determination adjustment model with higher accuracy may be obtained by training through a large amount of historical data.

In an alternative embodiment, the client may be triggered to make a local decision by the user behavior, and an intelligent decision may be made by the determination model to determine whether to send a request to the server.

It should be noted that the client may also initiate a request to the server by matching the user behavior with the fixed rule.

In the above embodiments of the present application, after target content in an information set is adjusted based on behavior data, content not included in the adjusted information set is stored in a local cache.

The above-mentioned non-contained contents may include: the rest content in the content sent by the server and the rest content in the adjusted information set.

In an optional embodiment, the number of unexposed contents in the information set is limited, and the number of contents sent by the server is large, so that a part of contents are left after the unexposed contents are adjusted, and at this time, the rest of contents can be stored in the content cache pool, so that after the point of interest of the user is changed, the contents in the content cache pool can be directly used for adjustment.

For example, still taking an e-commerce shopping scenario as an example, as shown in fig. 8, the number of the commodities requested to be supplemented by the cloud end by the client end is 6 (as shown by a solid box in the figure), one of the commodities needs to be supplemented into the unexposed content of the browsing list container, and the remaining 5 commodities are stored in the commodity cache pool. In addition, 3 remaining items (as indicated by the diagonal line boxes in the figure) which are replaced or have a large number in the browsing list container may also be stored in the item cache pool.

In the above embodiment of the present application, before adjusting the target content in the information set based on the behavior data, the method further includes the following steps: judging whether the behavior data meets a second condition; and if the behavior data meets the second condition, adjusting the target content in the information set based on the behavior data to obtain an adjusted information set.

The second condition described above may be a condition established to determine whether the set of information needs to be adjusted. For different user behaviors, not all user behaviors need to adjust the information set, for example, when the user views all orders, the information set contains all the orders of the user, and adjustment is not needed. In the embodiment of the present application, the information set may be adjusted in advance for a specific user behavior, and the specific user behavior may be information set sliding operation ending or the like.

In an alternative embodiment, after detecting the user behavior, the client may determine whether the user behavior is a specific user behavior, and if so, determine that the behavior data satisfies the second condition, further analyze the user intention, and adjust the information set.

In the above embodiment of the present application, before adjusting the target content in the information set based on the behavior data, the method further includes the following steps: receiving identification information of a trained model sent by a server, wherein the model comprises at least one of the following: processing the model, the first adjustment model, the second adjustment model and the determination model; and acquiring the trained model based on the identification information.

Compared with a server, the processing capacity of the client is weak, so that in order to avoid resource waste of the client, all models used by the client can be trained by the server in advance, and the models can be updated according to the feedback result of the client, so that models of multiple versions can be obtained.

In order to facilitate the client to obtain the model of the latest version, the server may distinguish the models of different versions through the identification information, where the identification information may be a version number of the model, or a number set in advance for the models of different versions, but is not limited thereto.

In an optional embodiment, the server may send identification information of a model that may be used by the client to the client, and after receiving the identification information, the client may obtain the model of the latest version from the model storage location based on the identification information, thereby improving the pushing effect.

A preferred embodiment of the present application will be described in detail below with reference to fig. 9 and 10, taking an e-commerce shopping scenario as an example.

As shown in fig. 9, 8 items are displayed in the information flow, and the remaining 6 items are unexposed items, namely item 0, item 1, item 2, item 3, item 4, and item 5. After the user clicks on the first item in the information stream, the item details can be viewed, including: browsing a large graph, entering evaluation, checking SKU (Stock Keeping Unit), shopping cart adding, purchasing and the like, storing user behavior data to an end-side data center after detecting the user behavior data, returning the user behavior data to an information stream, and continuing browsing by the user, wherein the intelligent decision triggering model can trigger the rearrangement model, the intelligent refreshing model and the user characteristic model to perform model operation. At this time, the unexposed commodity in the information stream and the cache pool commodity stored in the end-side commodity cache pool are taken as models to be involved, so as to obtain a final rearrangement result, and the unexposed commodity in the adjusted information stream is adjusted to be: commodity 8, commodity 9, commodity 7, commodity 4, commodity 2, and commodity 1. For the intelligent refreshing request, the cloud end can issue a new commodity, and for the local rearrangement, the request does not need to be sent to the cloud end. In addition, the redundant commodities in the information flow can be stored in the end-side commodity cache pool.

As shown in fig. 10, the whole process is as follows:

and step S1, issuing the machine learning model to the client through the server, and waiting to be triggered and executed by the user behavior.

Optionally, the information flow may request the end-side intelligent decision to obtain the version of each model, and obtain the corresponding model based on the version of each model.

And step S2, the information flow obtains long-term user characteristics and commodity characteristics calculated by the cloud through the request cloud.

Optionally, the information flow initiates a list request, uploads versions of the models to the information flow service end, and the information flow service end returns the data of the model-specific field list, and commodity features and user features of corresponding versions.

Step S3, capturing user behavior of the user in the information stream.

Step S4, storing the user behavior to the user local data.

Optionally, the user behavior triggers the user behavior management to collect the user behavior.

And step S5, judging whether intelligent decision is needed or not according to the user behavior.

Optionally, the user is triggered to make an intelligent decision after the sliding is finished.

And step S6, operating the user characteristic analysis model/intelligent refreshing model/local rearrangement model.

Optionally, each model on the user behavior trigger end side performs local decision, the model reads local data of the user, content data in the information stream and content in the local content cache pool, intelligent decision is comprehensively performed, user intention feature data are obtained, and the user intention feature data are stored locally.

And step S7, reading the locally stored user intention characteristic data and user behavior data.

Optionally, the model reads the local data of the user, the content data in the information stream, and the content in the local content cache pool, and performs an intelligent decision comprehensively to give a result whether to request or not and order the result.

Step S8, according to the intelligent refresh request result and the content rearrangement result, the intelligent refresh request is initiated and the content is rearranged.

Alternatively, the results of the predictions may be inferred based on the model for making decisions or changing the user content, such as initiating a request, or ordering, adding, deleting, etc. content data in the information stream.

And step S9, the information flow service end returns the current user preference commodity.

Optionally, the information flow service end calculates the current user preference commodity according to the user intention characteristic data carried in the request, and returns the result to the client.

In step S10, the information stream inserts the content requested to be returned into the position of the information stream that the user will see.

Optionally, the information flow acquisition request returns the commodity and merges the commodity into the unexposed list, the reserved position is unchanged, and the commodity after replacement is placed into the commodity cache pool.

And step S11, the client sorts the newly issued content according to the unexposed content and the cache pool content of the information flow, the content at the tail is eliminated, and the content more preferred by the user is left.

Alternatively, the number of hiddenBufferSize may be specified in advance, and the excess parts are eliminated and placed in the commodity cache pool.

Step S12, the adjusted information flow is presented through the UI.

Optionally, the final content results after the above deep learning and end-to-end sorting, culling, adding and deleting are presented to the user.

Through the steps, the favorite or disliked recommended content of the user can be sensed locally at millisecond level according to each interaction of the user, the end-side content can be changed at any time according to the preference of the user, and the preference of the user can be matched by adding, deleting, sequencing, content transformation and the like.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.

Example 2

There is also provided, in accordance with an embodiment of the present application, an information push method, it should be noted that the steps illustrated in the flowchart of the accompanying drawings may be executed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be executed in an order different from that herein.

Fig. 11 is a flowchart of another information pushing method according to an embodiment of the present application. As shown in fig. 11, the method includes the steps of:

in step S112, the client displays a part of the content of the information set in the preset area.

The preset area in the above step may be a display area of the information set on the client, and due to the limitation of the size of the interactive interface, the preset area can only display part of the content in the information set. The content displayed in the preset area is the exposure content in the information set.

The information set may be an information stream that is sent by the server to the client in advance and displayed to the user, and the information set may be determined based on an operation of the user. For example, in an e-commerce shopping scenario, if a user searches for a suit-dress, the information set may be all suit-dress goods delivered by the server. For another example, in a live broadcast scene, if the user searches for a women's clothing live broadcast room, the information set may be all live broadcast rooms of women's clothing sent by the server.

Step S114, after detecting the interaction behavior of the target object in the preset region, the client displays the adjusted information set on the preset region, where the adjusted information set is obtained by adjusting the target content in the information set based on the behavior data corresponding to the interaction behavior, and the target content is content in the information set that is not displayed in the preset region.

The above-mentioned interactive behavior may be an operation of clicking on a certain content in the information set by the user, or an operation of sliding the information set, but is not limited thereto.

In the above embodiment of the present application, the adjusted information set is obtained by adjusting the target content based on the current feature of the target object, and the current feature is obtained by processing the behavior data and the feature data corresponding to the target object by using a trained processing model, where the feature data includes: historical characteristics of the target object, and, characteristics of the historical content viewed by the target object.

In the above embodiment of the present application, in a case where it is determined to send a request to the server based on the current feature, the target content, and the second content stored in the local cache, the adjusted information set is obtained based on the target content, the second content, and a response returned by the server; in the case where it is determined not to send a request to the server based on the current characteristics, the target content, and the second content, the adjusted set of information is derived based on the current characteristics, the target content, and the second content.

In the above embodiment of the present application, the adjusted information set is obtained by processing the current feature, the target content, the second content, and the third content using the trained first adjustment model, or by processing the target content and the second content using the adjustment rule.

In the above embodiments of the present application, the adjusted information set is obtained by processing the current feature, the target content, and the second content using the trained second adjustment model.

In the above embodiments of the present application, whether to send a request to the server is determined by processing the current feature, the target content, and the second content using the trained determination model.

In the above embodiments of the present application, the content not included in the adjusted information set is stored in the local cache.

In the above embodiment of the present application, when the behavior data satisfies the second condition, the adjusted information set is obtained by adjusting the target content in the information set based on the behavior data.

In the above embodiment of the present application, the trained model is obtained based on identification information sent by a server, where the model includes at least one of the following: the method includes processing a model, a first adjusted model, a second adjusted model, and a determined model.

The preferred embodiments described in the above examples of the present application are the same as the schemes, application scenarios, and implementation procedures provided in example 1, but are not limited to the schemes provided in example 1.

Example 3

According to an embodiment of the present application, there is provided an information pushing apparatus for implementing the information pushing method, where the apparatus is deployed at a client, and as shown in fig. 12, the apparatus 1200 includes: a first obtaining module 1202, an adjusting module 1204, and a display module 1206.

The first obtaining module 1202 is configured to obtain behavior data of a target object in a preset area, where a part of content of an information set is displayed in the preset area; the adjusting module 1204 is configured to adjust target content in the information set based on the behavior data to obtain an adjusted information set, where the target content is content in the information set that is not displayed in a preset area; the display module 1206 is used for displaying the adjusted information set.

It should be noted here that the first obtaining module 1202, the adjusting module 1204 and the displaying module 1206 correspond to steps S302 to S306 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

In the above embodiments of the present application, the adjusting module includes: the device comprises an acquisition unit, a processing unit and an adjusting unit.

The obtaining unit is configured to obtain feature data corresponding to a target object stored in a server, where the feature data includes: the historical characteristics of the target object, and the characteristics of the historical content browsed by the target object; the processing unit is used for processing the behavior data and the characteristic data by using the trained processing model to obtain the current characteristic of the target object; the adjusting unit is used for adjusting the target content based on the current characteristics to obtain an adjusted information set.

In the above embodiment of the present application, the adjusting unit is further configured to determine whether to send a request to the server based on the current feature, the target content, and the second content stored in the local cache, where if it is determined to send the request to the server, an adjusted information set is obtained based on the target content, the second content, and a response returned by the server; if it is determined not to send a request to the server, an adjusted set of information is derived based on the current characteristics, the target content, and the second content.

In the above embodiment of the present application, the adjusting unit is further configured to process the current feature, the target content, the second content, and the third content by using the trained first adjusting model, so as to obtain an adjusted information set; or processing the target content and the second content by using the adjustment rule to obtain the adjusted information set.

In the foregoing embodiment of the present application, the adjusting unit is further configured to process the current feature, the target content, and the second content by using the trained second adjusting model, so as to obtain an adjusted information set.

In the above embodiment of the present application, the adjusting unit is further configured to process the current feature, the target content, and the second content by using the trained determination model, and determine whether to send a request to the server.

In the above embodiment of the present application, the apparatus further includes: and a storage module.

The storage module is used for adjusting the target content in the information set based on the behavior data and then storing the content which is not contained in the adjusted information set into the local cache.

In the above embodiment of the present application, the apparatus further includes: and a judging module.

The second judging module is used for judging whether the behavior data meets a second condition before adjusting the target content in the information set based on the behavior data; the adjusting module is further configured to adjust the target content in the information set based on the behavior data if the behavior data meets the second condition, so as to obtain an adjusted information set.

In the above embodiment of the present application, the apparatus further includes: the device comprises a receiving module and a second obtaining module.

The receiving module is used for receiving identification information of a trained model sent by a server before target content in an information set is adjusted based on behavior data, wherein the model comprises at least one of the following: processing the model, the first adjustment model, the second adjustment model and the determination model; the second obtaining module is used for obtaining the trained model based on the identification information.

It should be noted that the preferred embodiments described in the above examples of the present application are the same as the schemes, application scenarios, and implementation procedures provided in example 1, but are not limited to the schemes provided in example 1

Example 4

According to an embodiment of the present application, there is also provided an information pushing apparatus for implementing the information pushing method, where the apparatus is deployed at a client, and as shown in fig. 13, the apparatus 1300 includes: a first display module 1302 and a second display module 1304.

The first display module 1302 is configured to display a part of content of the information set in a preset area; the second display module 1304 is configured to display an adjusted information set on the preset region after detecting an interaction behavior of the target object in the preset region, where the adjusted information set is obtained by adjusting target content in the information set based on behavior data corresponding to the interaction behavior, and the target content is content in the information set that is not displayed in the preset region.

It should be noted here that the first display module 1302 and the second display module 1304 correspond to steps S112 to S114 in embodiment 2, and the two modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 1. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

Example 5

According to an embodiment of the present application, there is provided an information push system, as shown in fig. 14, the system including: a client 10 and a server 20.

Wherein, the server 20 is used for sending the information set; the client 10 is in communication with the server, and is configured to acquire behavior data of a target object in a preset area, adjust target content in the information set based on the behavior data to obtain an adjusted information set, and display the adjusted information set, where a part of content of the information set is displayed in the preset area, and the target content is content of the information set that is not displayed in the preset area.

In the foregoing embodiment of the present application, the client is further configured to obtain feature data corresponding to a target object stored in the server, process the behavior data and the feature data by using a trained processing model to obtain a current feature of the target object, and adjust target content based on the current feature to obtain an adjusted information set, where the feature data includes: historical characteristics of the target object, and, characteristics of the historical content viewed by the target object.

In the above embodiment of the present application, the client is further configured to determine whether to send a request to the server based on the current feature, the target content, and the second content stored in the local cache; the server is also used for returning a response to the client after receiving the request sent by the client; the client is also used for obtaining an adjusted information set based on the target content, the second content and the response; or, in the case that it is determined not to send the request to the server, obtaining the adjusted information set based on the current characteristics, the target content, and the second content.

In the above embodiment of the present application, the client is further configured to process the current feature, the target content, the second content, and the third content by using the trained first adjustment model, so as to obtain an adjusted information set; or processing the target content and the second content by using the adjustment rule to obtain the adjusted information set.

In the above embodiment of the present application, the client is further configured to process the current feature, the target content, and the second content by using the trained second adjustment model, so as to obtain an adjusted information set.

In the above embodiment of the present application, the client is further configured to process the current feature, the target content, and the second content by using the trained determination model, and determine whether to send a request to the server.

In the above embodiments of the present application, the client is further configured to store, in the local cache, content that is not included in the adjusted information set.

In the above embodiment of the present application, the client is further configured to determine whether the behavior data meets a second condition, where if the behavior data meets the second condition, the target content in the information set is adjusted based on the behavior data, so as to obtain an adjusted information set.

In the foregoing embodiment of the present application, the server is further configured to send identification information of the trained model, where the model includes at least one of: processing the model, the first adjustment model, the second adjustment model and the determination model; the client is further used for obtaining the trained model based on the identification information.

Example 6

According to an embodiment of the present application, there is provided a client, as shown in fig. 15, including: a display 12, an acquisition device 14, and a processor 16.

Wherein, the display 12 is used for displaying part of the content of the information set in a preset area; the acquisition device 14 is used for acquiring behavior data of the target object in a preset area; the processor 16 is connected to the display and the acquisition device, and is configured to adjust target content in the information set based on the behavior data to obtain an adjusted information set, and control the display to display the adjusted information set, where the target content is content in the information set that is not displayed in the preset area.

In the above-described embodiment of the present application, as shown in fig. 15, the processor 16 includes: a communication module 162, a processing module 164, and an adjustment module 166.

The communication module 162 is in communication connection with the server, and is configured to acquire feature data corresponding to a target object stored in the server, where the feature data includes: the historical characteristics of the target object, and the characteristics of the historical content browsed by the target object; the processing module 164 is connected to the acquisition device 14 and the communication module, and is configured to process the behavior data and the feature data by using a trained processing model to obtain a current feature of the target object; the adjusting module 166 is connected to the processing module and the display 12, and is configured to adjust the target content based on the current feature to obtain an adjusted information set.

In the above embodiment of the present application, the adjusting module is further configured to determine whether to send a request to the server based on the current feature, the target content, and the second content stored in the local cache; the communication module is also used for sending a request to the server and receiving a response returned by the server; the adjusting module is further used for obtaining an adjusted information set based on the target content, the second content and the response; or, in the case that it is determined not to send the request to the server, obtaining the adjusted information set based on the current characteristics, the target content, and the second content.

In the above embodiment of the present application, the adjusting module is further configured to process the current feature, the target content, the second content, and the third content by using the trained first adjusting model, so as to obtain an adjusted information set; or processing the target content and the second content by using the adjustment rule to obtain the adjusted information set.

In the above embodiment of the present application, the adjusting module is further configured to process the current feature, the target content, and the second content by using the trained second adjusting model, so as to obtain an adjusted information set.

In the above embodiment of the present application, the adjusting module is further configured to process the current feature, the target content, and the second content by using the trained determination model, and determine whether to send a request to the server.

In the above embodiment of the present application, as shown in fig. 15, the client further includes: a local cache 18.

The local cache 18 is connected to the adjusting module 166, and is configured to store content that is not included in the adjusted information set.

In the foregoing embodiment of the present application, the processor is further configured to determine whether the behavior data meets a second condition, where if the behavior data meets the second condition, the target content in the information set is adjusted based on the behavior data, so as to obtain an adjusted information set.

In the above embodiment of the present application, the communication module is further configured to receive identification information of a trained model sent by the server, where the model includes at least one of: processing the model, the first adjustment model, the second adjustment model and the determination model; the processor is further configured to obtain a trained model based on the identification information.

Example 7

According to an embodiment of the present application, an information push system is provided, including:

a processor; and

a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: acquiring behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area; adjusting target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in a preset area in the information set; and displaying the adjusted information set.

Example 8

The embodiment of the application can provide a computer terminal, and the computer terminal can be any one computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may also be replaced with a terminal device such as a mobile terminal.

Optionally, in this embodiment, the computer terminal may be located in at least one network device of a plurality of network devices of a computer network.

In this embodiment, the computer terminal may execute the program code of the following steps in the information pushing method: acquiring behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area; adjusting target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in a preset area in the information set; and displaying the adjusted information set.

Alternatively, fig. 16 is a block diagram of a computer terminal according to an embodiment of the present application. As shown in fig. 16, the computer terminal a may include: one or more processors 1602 (only one of which is shown), and a memory 1604.

The memory may be configured to store a software program and a module, such as program instructions/modules corresponding to the information pushing method and apparatus in the embodiment of the present application, and the processor executes various functional applications and data processing by running the software program and the module stored in the memory, so as to implement the information pushing method. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located from the processor, and these remote memories may be connected to terminal a through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area; adjusting target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in a preset area in the information set; and displaying the adjusted information set.

Optionally, the processor may further execute the program code of the following steps: acquiring feature data corresponding to a target object stored in a server, wherein the feature data comprises: the historical characteristics of the target object, and the characteristics of the historical content browsed by the target object; processing the behavior data and the characteristic data by using the trained processing model to obtain the current characteristic of the target object; and adjusting the target content based on the current characteristics to obtain an adjusted information set.

Optionally, the processor may further execute the program code of the following steps: determining whether to send a request to a server based on the current characteristics, the target content, and second content stored in the local cache; if the request is sent to the server, obtaining an adjusted information set based on the target content, the second content and the response returned by the server; if it is determined not to send a request to the server, an adjusted set of information is derived based on the current characteristics, the target content, and the second content.

Optionally, the processor may further execute the program code of the following steps: processing the current characteristics, the target content, the second content and the third content by using the trained first adjustment model to obtain an adjusted information set; or processing the target content and the second content by using the adjustment rule to obtain the adjusted information set.

Optionally, the processor may further execute the program code of the following steps: and processing the current characteristics, the target content and the second content by using the trained second adjustment model to obtain an adjusted information set.

Optionally, the processor may further execute the program code of the following steps: and processing the current characteristics, the target content and the second content by using the trained determination model to determine whether to send a request to the server.

Optionally, the processor may further execute the program code of the following steps: after the target content in the information set is adjusted based on the behavior data, the content which is not contained in the adjusted information set is stored in a local cache.

Optionally, the processor may further execute the program code of the following steps: before adjusting the target content in the information set based on the behavior data, judging whether the behavior data meets a second condition; and if the behavior data meets the second condition, adjusting the target content in the information set based on the behavior data to obtain an adjusted information set.

Optionally, the processor may further execute the program code of the following steps: before adjusting the target content in the information set based on the behavior data, the method further comprises the following steps: receiving identification information of a trained model sent by a server, wherein the model comprises at least one of the following: processing the model, the first adjustment model, the second adjustment model and the determination model; and acquiring the trained model based on the identification information.

By adopting the embodiment of the application, an information pushing scheme is provided. The user intention analysis is directly carried out locally through the client, and the content which is not displayed in the preset area in the information set is timely adjusted, so that the real-time identification of the user intention and the real-time updating of the pushed content are realized, the millisecond-level real-time response is given to each walking movement of the user by taking the user behavior as a trigger point, the recommendation browser body feeling of the user is improved, the recommendation effect is further improved, the technical effect of greatly reducing the resource consumption of the server is achieved, and the technical problem that the pushing effect is poor due to the fact that the client acquires the information set from the server at fixed time in the related technology is solved.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: displaying part of the content of the information set in a preset area; after the interactive behavior of the target object is detected in the information set, displaying the adjusted information set on the preset area, wherein the adjusted information set is obtained by adjusting the target content in the information set based on the behavior data corresponding to the interactive behavior, and the target content is the content which is not displayed in the preset area in the information set.

It can be understood by those skilled in the art that the structure shown in fig. 16 is only an illustration, and the computer terminal may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 16 is a diagram illustrating a structure of the electronic device. For example, the computer terminal a may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in fig. 16, or have a different configuration than shown in fig. 16.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

Example 9

Embodiments of the present application also provide a storage medium. Optionally, in this embodiment, the storage medium may be configured to store program codes executed by the information pushing method provided in the foregoing embodiment.

Optionally, in this embodiment, the storage medium may be located in any one of computer terminals in a computer terminal group in a computer network, or in any one of mobile terminals in a mobile terminal group.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: acquiring behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area; adjusting target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in a preset area in the information set; and displaying the adjusted information set.

Optionally, the storage medium is further configured to store program codes for performing the following steps: acquiring feature data corresponding to a target object stored in a server, wherein the feature data comprises: the historical characteristics of the target object, and the characteristics of the historical content browsed by the target object; processing the behavior data and the characteristic data by using the trained processing model to obtain the current characteristic of the target object; and adjusting the target content based on the current characteristics to obtain an adjusted information set.

Optionally, the storage medium is further configured to store program codes for performing the following steps: determining whether to send a request to a server based on the current characteristics, the target content, and second content stored in the local cache; if the request is sent to the server, obtaining an adjusted information set based on the target content, the second content and the response returned by the server; if it is determined not to send a request to the server, an adjusted set of information is derived based on the current characteristics, the target content, and the second content.

Optionally, the storage medium is further configured to store program codes for performing the following steps: processing the current characteristics, the target content, the second content and the third content by using the trained first adjustment model to obtain an adjusted information set; or processing the target content and the second content by using the adjustment rule to obtain the adjusted information set.

Optionally, the storage medium is further configured to store program codes for performing the following steps: and processing the current characteristics, the target content and the second content by using the trained second adjustment model to obtain an adjusted information set.

Optionally, the storage medium is further configured to store program codes for performing the following steps: and processing the current characteristics, the target content and the second content by using the trained determination model to determine whether to send a request to the server.

Optionally, the storage medium is further configured to store program codes for performing the following steps: after the target content in the information set is adjusted based on the behavior data, the content which is not contained in the adjusted information set is stored in a local cache.

Optionally, the storage medium is further configured to store program codes for performing the following steps: before adjusting the target content in the information set based on the behavior data, judging whether the behavior data meets a second condition; and if the behavior data meets the second condition, adjusting the target content in the information set based on the behavior data to obtain an adjusted information set.

Optionally, the storage medium is further configured to store program codes for performing the following steps: before adjusting the target content in the information set based on the behavior data, the method further comprises the following steps: receiving identification information of a trained model sent by a server, wherein the model comprises at least one of the following: processing the model, the first adjustment model, the second adjustment model and the determination model; and acquiring the trained model based on the identification information.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: displaying part of the content of the information set in a preset area; after the interactive behavior of the target object is detected in the information set, displaying the adjusted information set on the preset area, wherein the adjusted information set is obtained by adjusting the target content in the information set based on the behavior data corresponding to the interactive behavior, and the target content is the content which is not displayed in the preset area in the information set.

The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. An information push method, comprising:

the method comprises the steps that a client side obtains behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area;

the client adjusts target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in the preset area in the information set;

and the client displays the adjusted information set.

2. The method of claim 1, wherein adjusting the target content in the information set based on the behavior data to obtain an adjusted information set comprises:

acquiring feature data corresponding to the target object stored in a server, wherein the feature data comprises: the historical characteristics of the target object, and the characteristics of the historical content browsed by the target object;

processing the behavior data and the feature data by using a trained processing model to obtain the current feature of the target object;

and adjusting the target content based on the current characteristics to obtain the adjusted information set.

3. The method of claim 2, wherein adjusting the target content based on the current features to obtain the adjusted set of information comprises:

determining whether to send a request to the server based on the current characteristics, the target content, and second content stored in a local cache;

if the request is determined to be sent to the server, obtaining the adjusted information set based on the target content, the second content and a response returned by the server;

if it is determined that the request is not sent to the server, the adjusted information set is obtained based on the current feature, the target content, and the second content.

4. The method of claim 3, wherein the response comprises one of: and a third content or an adjustment rule, which is based on the target content, the second content and the response returned by the server to obtain the adjusted information set, wherein the adjusted information set comprises one of the following:

processing the current feature, the target content, the second content and the third content by using a trained first adjustment model to obtain the adjusted information set;

and processing the target content and the second content by using the adjusting rule to obtain the adjusted information set.

5. The method of claim 3, wherein deriving the adjusted set of information based on the current feature, the target content, and the second content comprises:

and processing the current characteristic, the target content and the second content by using a trained second adjustment model to obtain the adjusted information set.

6. The method of claim 3, wherein determining whether to send a request to the server based on the current characteristics, the target content, and second content stored in a local cache comprises:

and processing the current characteristic, the target content and the second content by using the trained determination model to determine whether to send the request to the server.

7. The method of claim 3, wherein the request comprises: the current characteristic or the behavior data satisfying a first condition.

8. The method of claim 1, wherein after adjusting the target content in the information set based on the behavior data, storing content not included in the adjusted information set in a local cache.

9. The method of claim 1, wherein prior to adjusting targeted content in the set of information based on the behavioral data, the method further comprises:

judging whether the behavior data meets a second condition;

and if the behavior data meets the second condition, adjusting the target content in the information set based on the behavior data to obtain the adjusted information set.

10. The method of claim 1, wherein prior to adjusting targeted content in the set of information based on the behavioral data, the method further comprises:

receiving identification information of a trained model sent by a server, wherein the model comprises at least one of the following: processing the model, the first adjustment model, the second adjustment model and the determination model;

and acquiring the trained model based on the identification information.

11. An information push method, comprising:

the client displays partial content of the information set in a preset area;

after detecting the interactive behavior of the target object in the preset area, the client displays an adjusted information set on the preset area, wherein the adjusted information set is obtained by adjusting target content in the information set based on behavior data corresponding to the interactive behavior, and the target content is content which is not displayed in the preset area in the information set.

12. The method of claim 11, wherein the adjusted information set is obtained by adjusting the target content based on a current feature of the target object, and the current feature is obtained by processing the behavior data and feature data corresponding to the target object by using a trained processing model, wherein the feature data includes: historical characteristics of the target object, and characteristics of historical content viewed by the target object.

13. The method of claim 12, wherein,

in the case where a request to be sent to a server is determined based on the current characteristics, the target content, and a second content stored in a local cache, the adjusted information set is obtained based on the target content, the second content, and a response returned by the server;

in an instance in which it is determined not to send the request to the server based on the current characteristics, the target content, and the second content, the adjusted set of information is derived based on the current characteristics, the target content, and the second content.

14. An information push system, comprising:

a server for sending a set of information;

the client is communicated with the server and used for acquiring behavior data of a target object in a preset area, adjusting target content in the information set based on the behavior data to obtain an adjusted information set, and displaying the adjusted information set, wherein partial content of the information set is displayed in the preset area, and the target content is content which is not displayed in the preset area in the information set.

15. The system of claim 14, wherein the client is further configured to obtain feature data corresponding to the target object stored in a server, process the behavior data and the feature data by using a trained processing model to obtain a current feature of the target object, and adjust the target content based on the current feature to obtain the adjusted information set, wherein the feature data includes: historical characteristics of the target object, and characteristics of historical content viewed by the target object.

16. The system of claim 15, wherein,

the client is further used for determining whether to send a request to the server based on the current characteristics, the target content and second content stored in a local cache;

the server is also used for returning a response to the client after receiving the request sent by the client;

the client is further configured to obtain the adjusted information set based on the target content, the second content, and the response; or, in a case where it is determined that the request is not sent to the server, obtaining the adjusted information set based on the current feature, the target content, and the second content.

17. A client, comprising:

a display for displaying a part of the content of the information set in a preset area;

the acquisition device is used for acquiring behavior data of the target object in the preset area;

and the processor is connected with the display and the acquisition device and used for adjusting target content in the information set based on the behavior data to obtain an adjusted information set and controlling the display to display the adjusted information set, wherein the target content is content which is not displayed in the preset area in the information set.

18. The client of claim 17, wherein the processor comprises:

the communication module is in communication connection with the server and is used for acquiring feature data corresponding to the target object stored in the server, wherein the feature data comprises: the historical characteristics of the target object, and the characteristics of the historical content browsed by the target object;

the processing module is connected with the acquisition device and the communication module and used for processing the behavior data and the characteristic data by using a trained processing model to obtain the current characteristic of the target object;

and the adjusting module is connected with the processing module and the display and used for adjusting the target content based on the current characteristics to obtain the adjusted information set.

19. The client according to claim 18, wherein,

the adjustment module is further configured to determine whether to send a request to the server based on the current characteristics, the target content, and second content stored in a local cache;

the communication module is also used for sending the request to the server and receiving a response returned by the server;

the adjusting module is further configured to obtain the adjusted information set based on the target content, the second content, and the response; or, in a case where it is determined that the request is not sent to the server, obtaining the adjusted information set based on the current feature, the target content, and the second content.

20. A storage medium comprising a stored program, wherein an apparatus in which the storage medium is located is controlled to execute the information push method according to any one of claims 1 to 13 when the program runs.

21. A computing terminal, comprising: a memory and a processor, the processor being configured to execute a program stored in the memory, wherein the program executes the information pushing method according to any one of claims 1 to 13.

22. An information push system, comprising:

a processor; and

a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: acquiring behavior data of a target object in a preset area, wherein partial content of an information set is displayed in the preset area; adjusting target content in the information set based on the behavior data to obtain an adjusted information set, wherein the target content is content which is not displayed in the preset area in the information set; and displaying the adjusted information set.