CN116866419A - Information pushing method, device, computer equipment and storage medium - Google Patents
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- G06Q30/06—Buying, selling or leasing transactions
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Abstract
The application relates to an information pushing method, an information pushing device, computer equipment and a storage medium. The method comprises the following steps: responding to the current interactive operation of the user aiming at the target application, and acquiring historical behavior data of the user; the historical behavior data comprises historical transaction information and a historical browsing path of a user; and processing the historical behavior data based on a path planning algorithm to obtain product pushing information aiming at the user, and pushing the product pushing information to the user. By adopting the method, the interaction instantaneity can be improved.
Description
Technical Field
The present application relates to the field of data mining, and in particular, to an information pushing method, an apparatus, a computer device, a storage medium, and a computer program product.
Background
With the development of data mining technology, information push technology is presented. At present, the information pushing method related to the financial products has the problem of poor interaction instantaneity, and the financial products meeting the user requirements can not be pushed to the user under the current interaction condition of the user.
The existing information pushing mode or the traditional method has the problem of poor interaction instantaneity.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an information pushing method, apparatus, computer device, computer readable storage medium, and computer program product that can improve interactive real-time.
In a first aspect, the present application provides an information pushing method, where the method includes:
responding to the current interactive operation of the user aiming at the target application, and acquiring historical behavior data of the user; the historical behavior data comprises historical transaction information and a historical browsing path of a user;
and processing the historical behavior data based on a path planning algorithm to obtain product pushing information aiming at the user, and pushing the product pushing information to the user.
In one embodiment, in response to a current interaction operation of a user with respect to a target application, acquiring historical behavior data of the user includes:
in response to a current interaction operation for the target application, determining whether the current interaction operation is a first interaction of the target application;
under the condition that the current interaction operation is determined to be the first interaction of the target application, collecting user information of a user;
based on the user information, historical behavior data is obtained.
In one embodiment, processing historical behavior data based on a path planning algorithm to obtain product push information for a user includes:
according to the historical behavior data, constructing an undirected graph of the historical behavior of the user;
processing the undirected graph based on a path planning algorithm, and confirming product preference data of a user;
and obtaining product pushing information aiming at the user according to the product preference data.
In one embodiment, constructing an undirected graph of historical behavior of a user based on historical behavior data includes:
generating a user behavior sequence of the user according to the historical behavior data; the user behavior sequence comprises a plurality of historical behavior nodes of the user;
noise reduction processing is carried out on the user behavior sequence to obtain an effective behavior sequence of the user; the user behavior sequence comprises a plurality of effective behavior nodes of the user;
and constructing an undirected graph based on each effective behavior node in the effective behavior sequence.
In one embodiment, the historical behavior data of the user further includes a historical dwell time of the user for at least one page address of the target application; based on each effective behavior node in the effective behavior sequence, constructing an undirected graph, including:
generating a plurality of nodes of the undirected graph based on each valid behavior node in the valid behavior sequence;
and generating weights among nodes in the undirected graph according to the historical residence time of the user.
In one embodiment, processing the undirected graph based on the path planning algorithm, identifying product preference data for the user includes:
generating a path point set based on each node and each weight in the undirected graph; the path point set comprises a plurality of predicted behavior tracks of the user;
determining a path starting point in the path point set according to the current interactive operation;
processing a path point set and a path starting point based on a path planning algorithm, and predicting at least one path end point in the path point set for a user;
and carrying out feature extraction processing on the path end point to obtain product preference data.
In one embodiment, the current interoperation includes at least one of: an access operation for at least one target page address, and a search operation for at least one target term.
In a second aspect, the present application provides an information pushing apparatus. The device comprises:
the user data acquisition module is used for responding to the current interactive operation of the user aiming at the target application and acquiring the historical behavior data of the user; the historical behavior data comprises historical transaction information and a historical browsing path of a user;
and the product information pushing module is used for processing the historical behavior data based on the path planning algorithm to obtain product pushing information aiming at the user and pushing the product pushing information to the user.
In a third aspect, the present application provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
In a fifth aspect, the present application provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of the method described above.
According to the information pushing method, the information pushing device, the computer equipment, the storage medium and the computer program product, by responding to the current interactive operation of the user for the target application and acquiring the historical behavior data comprising the historical transaction information and the historical browsing path of the user, the historical behavior data of the user can be acquired in the initial interaction with the user, so that the preference product of the user can be predicted; the historical behavior data is processed based on a path planning algorithm, product pushing information aiming at a user is obtained, the product pushing information is pushed to the user, preference products of the user can be predicted when the user interacts with the user for the first time, pushing is carried out aiming at pain points of the user according to the historical behavior of the user, the interaction instantaneity of pushing is improved, and the success rate of interaction is further improved; the accuracy of the pushing can also be gradually increased in subsequent interactions with the user.
Drawings
FIG. 1 is an application environment diagram of an information push method in one embodiment;
FIG. 2 is a flow chart of a method for pushing information in one embodiment;
FIG. 3 is a flow chart illustrating a message pushing step in one embodiment;
fig. 4 is a schematic flow chart of an information pushing step in another embodiment;
FIG. 5 is a schematic diagram of an undirected graph in one embodiment;
FIG. 6 is a flowchart illustrating a message pushing step in yet another embodiment;
FIG. 7 is a flowchart of an information pushing step according to still another embodiment;
FIG. 8 is a flow chart illustrating a message pushing step in one embodiment;
FIG. 9 is a block diagram of an information pushing device in one embodiment;
fig. 10 is an internal structural view of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, the method provided by the embodiment of the application can be applied to an application environment as shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may obtain historical behavior data of the user from the server 104 in response to a current interaction operation of the user for the target application; the terminal 102 may also process the historical behavior data based on a path planning algorithm to obtain product push information for the user, and push the product push information to the user. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, among others. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, there is provided an information pushing method, including:
step 210, responding to the current interactive operation of the user aiming at the target application, and acquiring historical behavior data of the user; the historical behavior data comprises historical transaction information and a historical browsing path of a user;
specifically, the target application may be configured at a user end, for example, a terminal, and the user may perform corresponding interaction with respect to the target application configured at the terminal; further, in response to the current interactive operation of the user for the target application, the terminal can acquire the historical behavior data of the user by sending a user information acquisition request to acquire the historical behavior data of the user, for example, the historical behavior data locally imported by the user can comprise historical transaction information and a historical browsing path for the target application and also can comprise historical transaction information and a historical browsing path for other applications; the terminal may also send a historical behavior data acquisition request to the server to receive the historical behavior data of the user in the database sent by the server, for example, the historical behavior data of the user may include the historical transaction information and the historical browsing path acquired through the target application and stored in the server, and may also include the historical transaction information and the historical browsing path acquired through other applications and stored in the server. By acquiring historical behavior data of the user in response to the current interaction operation of the user with respect to the target application, the historical behavior data of the user can be acquired at the time of the initial interaction with the user, so that the prediction of the preference product of the user can be made.
In some examples, the target Application may include an Application (APP), a website, or the like.
Step 220, historical behavior data is processed based on a path planning algorithm, product pushing information aiming at the user is obtained, and the product pushing information is pushed to the user.
Specifically, the historical behavior data can be processed based on a path planning algorithm, the path planning algorithm can be a path planning algorithm based on graph search, the historical behavior data of the user can be abstracted into a directed graph or undirected graph model, the model has a large amount of data value, the path planning algorithm can be used for extracting so as to predict products according with user preferences, product pushing information for pushing to the user is determined, and the product pushing information is pushed to the user. The historical behavior data is processed based on a path planning algorithm to obtain product pushing information aiming at the user, the product pushing information is pushed to the user, the preference product of the user can be predicted when the user interacts with the user for the first time, pushing is performed aiming at pain points of the user according to the historical behavior of the user, the interaction instantaneity of pushing is improved, and the success rate of interaction is further improved; the accuracy of the pushing can also be gradually increased in subsequent interactions with the user.
In some examples, the path planning Algorithm may include a probabilistic Algorithm (a-star Algorithm), where the conventional mining Algorithm is slow and difficult to have high real-time, and the probabilistic Algorithm may efficiently improve the time of graph mining and quickly determine product pushing information for pushing to the user; the method can also add a strategy of randomness and multiple iterations when searching by adopting a probability Astar algorithm, so that the graph mining is balanced in running time and accuracy, a plurality of approximate optimal solutions can be obtained, and further more comprehensive information pushing is realized.
According to the embodiment of the application, the historical behavior data of the user can be obtained in the primary interaction with the user by responding to the current interaction operation of the user aiming at the target application and obtaining the historical transaction information of the user and the historical behavior data of the historical browsing path, so that the preference product of the user can be predicted conveniently; the historical behavior data is processed based on a path planning algorithm, product pushing information aiming at a user is obtained, the product pushing information is pushed to the user, preference products of the user can be predicted when the user interacts with the user for the first time, pushing is carried out aiming at pain points of the user according to the historical behavior of the user, the interaction instantaneity of pushing is improved, and the success rate of interaction is further improved; the accuracy of the pushing can also be gradually increased in subsequent interactions with the user.
In one embodiment, as shown in fig. 3, in response to a current interaction operation of a user with respect to a target application, acquiring historical behavior data of the user includes:
step 310, in response to the current interaction operation for the target application, determining whether the current interaction operation is the first interaction of the target application;
step 320, under the condition that the current interaction operation is determined to be the first interaction of the target application, collecting user information of the user;
step 330, based on the user information, historical behavior data is obtained.
Specifically, in response to a current interaction operation of the user with respect to the target application, for example, in response to at least one of a click operation of the user with respect to the target application and an input operation of the user with respect to the target application, it may be determined whether the current interaction operation is a first interaction of the target application; the first interaction of the target application can be the first interaction after the target application is installed by the terminal, and can also be the first interaction when the user logs in the target application; under the condition that the current interaction operation is determined to be the first interaction of the target application, user information of the user can be acquired, for example, the user information of the user can be acquired by sending a user information acquisition request to the user, wherein the user information of the user can comprise historical behavior data of the user, and the historical behavior data can be the historical behavior data of the user stored locally or can be the historical behavior data of the user stored by a server; the historical behavior data of the user may include historical transaction information and historical browsing paths for the target application, and may also include historical transaction information and historical browsing paths for other applications. Through the acquired user information, historical behavior data of the user can be obtained. By the method, the historical behavior data of the user can be acquired in the first interaction with the user, so that the prediction of the preference product of the user can be conveniently made.
In some examples, the current interaction comprises at least one of: an access operation for at least one target page address, and a search operation for at least one target term.
In one embodiment, as shown in fig. 4, the processing the historical behavior data based on the path planning algorithm to obtain the product push information for the user includes:
step 410, constructing an undirected graph of the historical behavior of the user according to the historical behavior data;
step 420, processing the undirected graph based on the path planning algorithm, and confirming the product preference data of the user;
and step 430, obtaining product pushing information for the user according to the product preference data.
Specifically, an undirected graph of the historical behaviors of the user may be constructed according to the historical behavior data of the user, where each node of the undirected graph may correspond to one or more historical behaviors of the user, and further an undirected graph of the historical behaviors of the user (numbering is used to mark each node in the undirected graph) as shown in fig. 5 may be constructed according to the relationship between the historical behaviors. Further, the undirected graph can be processed based on a path planning algorithm, and product preference data of the user can be confirmed, for example, the product preference data of the user is predicted based on the current interactive operation of the user, wherein the product preference data is used for reflecting the behavior intention or the product preference of the user, and further product pushing information aiming at the user can be obtained according to the product preference data. By the method, the product pushing information aiming at the user can be obtained quickly.
In some examples, the product may provide the target application with a product for resource exchange, such as various types of financial products. The product pushing information for the user can be obtained according to the similarity between the product preference data and the information of the currently provided product, for example, the product pushing information of at least one product with the highest similarity among the product preference data and the currently provided product can be pushed to the user.
In one embodiment, as shown in fig. 6, constructing an undirected graph of historical behavior of a user based on historical behavior data, includes:
step 610, generating a user behavior sequence of the user according to the historical behavior data; the user behavior sequence comprises a plurality of historical behavior nodes of the user;
step 620, performing noise reduction processing on the user behavior sequence to obtain an effective behavior sequence of the user; the user behavior sequence comprises a plurality of effective behavior nodes of the user;
step 630, constructing an undirected graph based on each valid behavior node in the valid behavior sequence.
Specifically, a user behavior sequence of the user may be generated according to historical behavior data of the user, where the user behavior sequence includes a plurality of historical behavior nodes of the user, and each of the historical behavior nodes may correspond to one or more user behaviors of the user; the user behavior sequence can be subjected to noise reduction processing to filter historical behavior nodes which are not used for reflecting the user behavior intention and the product preference, so that an effective behavior sequence of the user is obtained, the user behavior sequence comprises a plurality of effective behavior nodes of the user, and each effective behavior node plays a corresponding role in reflecting the user behavior intention and the product preference. Further, an undirected graph can be constructed based on each valid behavior node in the valid behavior sequence, and each node in the undirected graph can be generated based on each valid behavior node. In this way, an undirected graph can be generated for predicting the user's preferred products, reflecting the links between the effective behaviors with a high degree of accuracy in reflecting the user's behavioral intent and product preferences.
In some examples, the noise reduction processing is performed on the user behavior sequence, for example, classification or quantization processing may be performed on each user behavior to filter out historical behavior nodes corresponding to user behaviors in which user refreshing, user offline, and the like are difficult to reflect user behavior intent and product preference.
In one embodiment, the historical behavior data of the user further includes a historical dwell time of the user for at least one page address of the target application; as shown in fig. 7, constructing an undirected graph based on each valid behavior node in the valid behavior sequence includes:
step 710, generating a plurality of nodes of the undirected graph based on each valid behavior node in the valid behavior sequence;
and 720, generating weights among nodes in the undirected graph according to the historical residence time of the user.
In particular, multiple nodes of the undirected graph may be generated based on each valid behavior node in the valid behavior sequence, e.g., one of the nodes of the undirected graph may be generated from one or more valid behavior nodes; the historical residence time of the user can be used for generating the weight among all nodes in the undirected graph, wherein the historical residence time of the user can be the time between two adjacent historical behaviors of the user, and then the historical residence time of the user can be mapped to a path among adjacent nodes in the undirected graph to determine the weight among all the connected nodes in the undirected graph. By the method, the relation among the effective behaviors can be reflected more accurately, and the method is used for more accurately predicting the preference products of the users.
In one embodiment, as shown in fig. 8, the processing of the undirected graph based on the path planning algorithm, to confirm the product preference data of the user, includes:
step 810, generating a path point set based on each node and each weight in the undirected graph; the path point set comprises a plurality of predicted behavior tracks of the user;
step 820, determining a path starting point in the set of path points according to the current interaction operation;
step 830, processing the path point set and the path start point based on the path planning algorithm, and predicting at least one path end point in the path point set for the user;
and 840, performing feature extraction processing on the path end point to obtain product preference data.
In particular, a set of path points comprising a plurality of predicted behavior trajectories of the user may be generated based on the nodes and weights in the undirected graph, i.e. the set of path points may comprise all feasible paths in the undirected graph; further, a path starting point in the path point set can be determined according to the current interaction operation, the current interaction operation can be regarded as a node in the undirected graph, the node can be determined as the path starting point in the path point set, and the path starting point can correspond to at least one predicted behavior track; processing a path point set and a path starting point based on a path planning algorithm, and predicting at least one optimal predicted behavior track for a user, so as to obtain a path end point corresponding to the optimal predicted behavior track, wherein the path end point can be used for predicting final decision information of the user; by performing feature extraction processing on the path end point, product preference data can be obtained, and the product preference data can predict the behavior intention or the product preference of the user. By the method, the product preference data of the user can be rapidly predicted.
In some examples, the product preference data may include at least one preference product for resource exchange with the predicted intent of the user, and by obtaining the product preference data of the user, pushing may be performed for the pain point of the user, thereby improving the success rate of pushing.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an information pushing device for realizing the above related information pushing method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in one or more embodiments of the information pushing device provided below may refer to the limitation of the information pushing method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 9, an information pushing apparatus is provided. The device comprises:
a user data obtaining module 910, configured to obtain historical behavior data of a user in response to a current interactive operation of the user with respect to a target application; the historical behavior data comprises historical transaction information and a historical browsing path of a user;
the product information pushing module 920 is configured to process the historical behavior data based on the path planning algorithm, obtain product pushing information for the user, and push the product pushing information to the user.
In one embodiment, the user data acquisition module 910 is further configured to determine, in response to a current interaction operation for the target application, whether the current interaction operation is a first interaction of the target application; under the condition that the current interaction operation is determined to be the first interaction of the target application, collecting user information of a user; based on the user information, historical behavior data is obtained.
In one embodiment, the product information pushing module 920 is further configured to construct an undirected graph of the historical behavior of the user according to the historical behavior data; processing the undirected graph based on a path planning algorithm, and confirming product preference data of a user; and obtaining product pushing information aiming at the user according to the product preference data.
In one embodiment, the product information pushing module 920 is further configured to generate a user behavior sequence of the user according to the historical behavior data; the user behavior sequence comprises a plurality of historical behavior nodes of the user; noise reduction processing is carried out on the user behavior sequence to obtain an effective behavior sequence of the user; the user behavior sequence comprises a plurality of effective behavior nodes of the user; and constructing an undirected graph based on each effective behavior node in the effective behavior sequence.
In one embodiment, the historical behavior data of the user further includes a historical dwell time of the user for at least one page address of the target application; the product information pushing module 920 is further configured to generate a plurality of nodes of the undirected graph based on each valid behavior node in the valid behavior sequence; and generating weights among nodes in the undirected graph according to the historical residence time of the user.
In one embodiment, the product information pushing module 920 is further configured to generate a set of path points based on each node and each weight in the undirected graph; the path point set comprises a plurality of predicted behavior tracks of the user; determining a path starting point in the path point set according to the current interactive operation; processing a path point set and a path starting point based on a path planning algorithm, and predicting at least one path end point in the path point set for a user; and carrying out feature extraction processing on the path end point to obtain product preference data.
The modules in the information pushing device may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided. The computer device comprises a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
In one embodiment, a computer device is provided, which may be a terminal, and an internal structure diagram thereof may be as shown in fig. 10. The computer device includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input means. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement an information push method. The display unit of the computer device is used for forming a visual picture, and can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer-readable storage medium is provided. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.
In one embodiment, a computer program product is provided. The computer program product comprises a computer program which, when executed by a processor, implements the steps of the method described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (11)
1. An information pushing method, characterized in that the method comprises:
responding to the current interactive operation of a user aiming at a target application, and acquiring historical behavior data of the user; the historical behavior data comprises historical transaction information and a historical browsing path of the user;
and processing the historical behavior data based on a path planning algorithm to obtain product pushing information aiming at the user, and pushing the product pushing information to the user.
2. The method of claim 1, wherein the obtaining historical behavior data of the user in response to the user's current interaction with the target application comprises:
determining, in response to the current interaction operation for the target application, whether the current interaction operation is a first interaction of the target application;
under the condition that the current interaction operation is determined to be the first interaction of the target application, collecting user information of the user;
and obtaining the historical behavior data based on the user information.
3. The method of claim 1, wherein the processing the historical behavioral data based on a path planning algorithm to obtain product push information for the user comprises:
according to the historical behavior data, constructing an undirected graph of the historical behavior of the user;
processing the undirected graph based on the path planning algorithm, and confirming product preference data of the user;
and obtaining the product pushing information aiming at the user according to the product preference data.
4. A method according to claim 3, wherein said constructing an undirected graph of the historical behaviour of the user from the historical behaviour data comprises:
generating a user behavior sequence of the user according to the historical behavior data; the user behavior sequence comprises a plurality of historical behavior nodes of the user;
carrying out noise reduction treatment on the user behavior sequence to obtain an effective behavior sequence of the user; the user behavior sequence comprises a plurality of effective behavior nodes of the user;
and constructing the undirected graph based on each effective behavior node in the effective behavior sequence.
5. The method of claim 4, wherein the historical behavior data of the user further comprises a historical dwell time of the user for at least one page address of the target application; the constructing the undirected graph based on each valid behavior node in the valid behavior sequence includes:
generating a plurality of nodes of the undirected graph based on each of the valid behavior nodes in the valid behavior sequence;
and generating weights among nodes in the undirected graph according to the historical residence time of the user.
6. The method of claim 5, wherein said processing said undirected graph based on said path planning algorithm to identify product preference data for said user comprises:
generating a path point set based on each node in the undirected graph and each weight; the set of path points includes a plurality of predicted behavior trajectories for the user;
determining a path starting point in the path point set according to the current interaction operation;
processing the path point set and the path starting point based on a path planning algorithm, and predicting at least one path ending point in the path point set for the user;
and carrying out feature extraction processing on the path end point to obtain the product preference data.
7. The method of any one of claims 1 to 6, wherein the current interoperation comprises at least one of: an access operation for at least one target page address, and a search operation for at least one target term.
8. An information pushing apparatus, characterized in that the apparatus comprises:
the user data acquisition module is used for responding to the current interactive operation of the user aiming at the target application and acquiring the historical behavior data of the user; the historical behavior data comprises historical transaction information and a historical browsing path of the user;
and the product information pushing module is used for processing the historical behavior data based on a path planning algorithm to obtain product pushing information aiming at the user and pushing the product pushing information to the user.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310632900.7A CN116866419A (en) | 2023-05-31 | 2023-05-31 | Information pushing method, device, computer equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310632900.7A CN116866419A (en) | 2023-05-31 | 2023-05-31 | Information pushing method, device, computer equipment and storage medium |
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| CN202310632900.7A Pending CN116866419A (en) | 2023-05-31 | 2023-05-31 | Information pushing method, device, computer equipment and storage medium |
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