CN115409581A

CN115409581A - Product recommendation method, device, equipment and medium based on user behavior configuration

Info

Publication number: CN115409581A
Application number: CN202211121757.7A
Authority: CN
Inventors: 周开用
Original assignee: Ping An Consumer Finance Co Ltd
Current assignee: Ping An Consumer Finance Co Ltd
Priority date: 2022-09-15
Filing date: 2022-09-15
Publication date: 2022-11-29

Abstract

The invention relates to an artificial intelligence technology, and discloses a product recommendation method based on user behavior configuration, which comprises the following steps: acquiring historical browsing records and browsing duration of a user mobile phone APP, and generating a user behavior record table; constructing a batch configuration table and building a big data batch service; analyzing the behavior of the user to obtain the browsing preference of the user, configuring the attribute of the main push product according to the browsing preference of the user, and generating a main push product attribute table; and selecting recommended products from the product table, filling the recommended products into a preset recommended table, establishing the recommended table as an automatic user recommended table, and pushing the recommended products in the automatic user recommended table to a user mobile phone APP. In addition, the invention also relates to a block chain technology, and the browsing preference and the product attribute can be stored in the nodes of the block chain. The invention also provides a product recommendation device based on user behavior configuration, electronic equipment and a storage medium. The invention can improve the product recommendation accuracy.

Description

Product recommendation method, device, equipment and medium based on user behavior configuration

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to a product recommendation method and device based on user behavior configuration, electronic equipment and a computer readable storage medium.

Background

With the continuous development of the consumption concept of modern people, loan consumption is a common consumption form, and advanced consumption or investment or house vehicle purchase is selected within the range of repayment. In order to deliver appropriate loan services to different users, user behaviors need to be analyzed, and the appropriateness of service recommendation is improved.

In the existing products pushed by loans of various sizes on the market, most products are recommended only according to the content of the products, the actual requirements of different users cannot be considered, whether the user requirements are matched with the product attributes or not is judged, the purchase rate of the users in practical application is low, and the users cannot accurately recommend the products.

Disclosure of Invention

The invention provides a product recommendation method and device based on user behavior configuration and a computer readable storage medium, and mainly aims to solve the problem of low precision in product recommendation.

In order to achieve the above object, the present invention provides a product recommendation method based on user behavior configuration, including:

acquiring a historical browsing record and browsing duration of a user mobile phone APP by using a preset embedded point service, and generating a user behavior record table according to the historical browsing record and the browsing duration;

configuring a batch running factor by using a preset background configuration system, constructing a batch running configuration table according to the batch running factor, and constructing a big data batch running service according to the batch running configuration table;

analyzing the behavior of the user according to the big data batch service to obtain the browsing preference of the user, configuring the attribute of a main push product according to the browsing preference of the user, and establishing a main push product attribute table according to the attribute of the main push product;

and selecting a recommended product from a preset product table according to the main push product attribute table, filling the recommended product into a preset recommended table, establishing the recommended table as a user automatic recommended table, and pushing the recommended product in the user automatic recommended table to a user mobile phone APP.

Optionally, the product recommendation method based on user behavior configuration is characterized in that the method includes the steps of obtaining a historical browsing record and browsing duration of a user mobile phone APP by using a preset embedded point service, and generating a user behavior record table according to the historical browsing record and the browsing duration, and includes:

acquiring a preset embedded point service;

and recording the historical browsing record and the browsing duration extracted by the embedded point service in a preset first blank table to obtain a user behavior record table.

Optionally, the product recommendation method based on user behavior configuration is characterized in that the obtaining of the preset embedded point service includes:

acquiring a corresponding embedded point element of a preset embedded point requirement in a user mobile phone;

acquiring an information API of the embedded point element;

and calling the buried point service from the information processing system according to the information API.

Optionally, the product recommendation method based on user behavior configuration is characterized in that the building of the batch configuration table according to the batch factor and the building of the big data batch service according to the batch configuration table includes:

acquiring configuration factors in a preset background configuration system and factor data corresponding to the configuration factors;

filling the configuration factor and the factor data into a preset second blank table to obtain a batch configuration table;

building a big data batch service framework according to the batch configuration table;

and configuring a preset batch running rule in the big data batch running service framework to obtain the big data batch running service.

Optionally, the analyzing the behavior of the user according to the big data batching service to obtain the browsing preference of the user includes:

counting the occurrence frequency of the user behavior corresponding to each data in the user behavior record table by using big data batch service;

and determining the user behavior exceeding the preset frequency value as the browsing preference of the user.

Optionally, the configuring, according to the browsing preference of the user, the attribute of the push product, and establishing a push product attribute table according to the attribute of the push product, includes:

acquiring preference participles of browsing preference of a user, and converting the preference participles into preference vectors;

obtaining product participles with preset product attributes, and converting the product participles into product vectors;

calculating the matching degree of the preference vector and the product vector by using a matching degree calculation formula;

selecting products corresponding to the product vectors exceeding the preset matching degree as main pushing products;

and filling product attributes corresponding to the main pushing product into a preset third blank table, and naming the third blank table as a main pushing product attribute table.

Optionally, the selecting a recommended product from a preset product table according to the master pushed product attribute table, filling the recommended product into a preset recommendation table, and establishing the recommendation table as an automatic user recommendation table includes:

establishing a main push product decision tree model according to a main push product attribute table;

inputting the attributes of the main push products into the decision tree model one by one;

summarizing the product records obtained each time in a preset recommendation table, and deleting redundant same products in the recommendation table;

and the recommendation table is the user recommendation table, and the establishment of the user automatic recommendation table is completed.

In order to solve the above problem, the present invention further provides a product recommendation device configured based on user behavior, the device comprising:

a behavior recording module: acquiring a historical browsing record and browsing duration of a user mobile phone APP by using a preset embedded point service, and generating a user behavior record table according to the historical browsing record and the browsing duration;

establishing a service module: configuring a batch running factor by using a preset background configuration system, constructing a batch running configuration table according to the batch running factor, and constructing a big data batch running service according to the batch running configuration table;

constructing a table module: analyzing the behavior of the user according to the big data batch service to obtain the browsing preference of the user, configuring the attribute of a main push product according to the browsing preference of the user, and establishing a main push product attribute table according to the attribute of the main push product;

an automatic pushing module: and selecting a recommended product from a preset product table according to the main push product attribute table, filling the recommended product into a preset recommended table, establishing the recommended table as a user automatic recommended table, and pushing the recommended product in the user automatic recommended table to a user mobile phone APP.

In order to solve the above problem, the present invention also provides an electronic device, including:

at least one processor;

and a memory communicatively coupled to the at least one processor;

wherein the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the user behavior-configured based product recommendation method described above.

In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the product recommendation method configured based on user behavior.

The method and the device for establishing the batch running service acquire behavior data of a user through a preset embedded point service, configure batch running factors through a background configuration system, and establish the batch running service according to the batch running factors; analyzing the behavior data of the user by using the batch running service to obtain a preferred product of the user; taking product contents corresponding to different product attributes, and pushing the products according to user preferences; by the method, products required and interested by the user can be calculated to a large extent, so that the accuracy of product recommendation is improved. Therefore, the product recommendation method, the product recommendation device, the electronic equipment and the computer readable storage medium based on user behavior configuration, provided by the invention, can solve the problem of low precision in product recommendation.

Drawings

Fig. 1 is a schematic flowchart of a product recommendation method configured based on user behavior according to an embodiment of the present invention;

fig. 2 is a schematic flow chart illustrating a process of acquiring a buried point service according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of building a batch service according to an embodiment of the present invention;

FIG. 4 is a functional block diagram of a product recommendation device configured based on user behavior according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device implementing the product recommendation method configured based on user behavior according to an embodiment of the present invention.

The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.

The embodiment of the application provides a product recommendation method based on user behavior configuration. The execution subject of the product recommendation method configured based on the user behavior includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the product recommendation method configured based on user behavior may be executed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.

Referring to fig. 1, a flowchart of a product recommendation method configured based on user behavior according to an embodiment of the present invention is shown. In this embodiment, the product recommendation method configured based on user behavior includes:

s1, obtaining a historical browsing record and browsing duration of a user mobile phone APP by using a preset embedded point service, and generating a user behavior record table according to the historical browsing record and the browsing duration;

in the embodiment of the invention, the embedded point refers to a relevant technology for capturing, processing and sending a specific user behavior or event and an implementation process thereof. The embedded point service in the invention is actually a data acquisition process, and specific user behaviors and events are the key points of acquisition, for example, in the example, historical browsing records of a user mobile phone APP are obtained.

In detail, the method for obtaining the historical browsing record and the browsing duration of the user mobile phone APP by using the preset embedded point service and generating the user behavior record table according to the historical browsing record and the browsing duration comprises the following steps: acquiring a preset buried point service; and recording the historical browsing record and the browsing duration extracted by the embedded point service in a preset first blank table to obtain a user behavior record table.

Specifically, referring to fig. 2, the acquiring of the preset buried point service includes:

s21, acquiring a corresponding embedded point element of a preset embedded point requirement in a user mobile phone;

s22, obtaining an information API of the embedded point element;

and S23, calling the buried point service from the information processing system according to the information API.

In detail, the message processing system includes, but is not limited to, kafka, JMS, and the like.

Additionally, the buried point service may be divided into a manual buried point and an automatic buried point. Wherein manual bury the data that the point needs manpower analysis to acquire, the position of burying a point element is gone out to the manpower analysis, produces the error easily, is unfavorable for subsequent operation. Therefore, the method of automatic point burying is adopted, and the corresponding buried point elements are found out according to the preset buried point requirements, so that the obtained result is accurate, the process is simple, and the time for acquiring data is reduced.

In detail, the obtaining of the corresponding embedded point element of the preset embedded point requirement in the mobile phone of the user includes: performing word segmentation operation on the buried point requirements to obtain buried point word segmentation; calculating the weights of the buried point participles one by one, and taking the buried point participles exceeding a preset weight threshold value as buried point keywords required by the buried point; searching the buried point keywords in a preset buried point metadata base, and determining the buried point elements corresponding to the found buried point keywords as the buried point elements corresponding to the preset buried point requirements.

Specifically, the embedded point requirement is firstly obtained to confirm the problem to be solved or the expected effect to be achieved by the embedded point service, in the case of the present invention, the historical browsing record and the browsing duration of the mobile phone APP of the user need to be obtained, and the embedded point element may be a button for entering the page by clicking and a button for exiting the page by clicking.

Specifically, the step of obtaining the browsing record and the browsing duration of the user APP by using the embedded point service is simple in operation, the obtained information is accurate, automatic backup can be generated and stored in a log file, the information is not easy to lose, and the data processing method is stable.

S2, configuring a batch running factor by using a preset background configuration system, constructing a batch running configuration table according to the batch running factor, and constructing a big data batch running service according to the batch running configuration table;

in the embodiment of the invention, if the user mobile phone APP is a palm bank system of a certain bank, the batch running factor can be the time limit, exchange rate, deposit time, recommended number and the like of deposit business.

In the embodiment of the invention, the big data batch service is a service for accumulating data to be processed into batches in the big data background and processing the batches at one time at a set time, and is called a big data batch processing service, namely a big data batch service.

In the embodiment of the present invention, referring to fig. 3, the building of the batch configuration table according to the batch factor and the building of the big data batch service according to the batch configuration table includes:

s31, acquiring configuration factors in a preset background configuration system and factor data corresponding to the configuration factors;

s32, filling the configuration factor and the factor data into a preset second blank table to obtain a batch configuration table;

s33, building a big data batch service framework according to the batch configuration table;

and S34, configuring a preset batch running rule in the big data batch running service framework to obtain the big data batch running service.

Specifically, since the configuration factors usually have specific numbers corresponding to each other, for example, if one of the configuration factors is a bank exchange rate, specific values of different exchange rates corresponding to different currencies of a certain day of a certain bank can be accurately obtained, and the specific values are factor data corresponding to the configuration factors.

Additionally, the batch rule may be a calculation formula for calculating the factor data, which specifies what operation is to be performed on data when the big data batch service runs, and is a core of the big data batch service, and the batch service needs to perform calculation according to the batch rule when processing data.

In detail, when batch services are processed in batch, the batch services are still efficient and orderly under the complex environment of cross-system cross-platform and service logic guarantee, so that the low efficiency in manual operation is avoided, and the error probability is reduced. The use of batch services will play a key role in improving the operating efficiency of the system, reducing the system cost, and improving the quality of service.

S3, analyzing the behavior of the user according to the big data batch service to obtain the browsing preference of the user, configuring the attribute of a main push product according to the browsing preference of the user, and establishing a main push product attribute table according to the attribute of the main push product;

because the preference of the user is difficult to predict and the preference content of different users is different, the behavior of the user needs to be analyzed by using big data batch service, and the browsing preference of the user is extracted from the user behavior, so that automatic recommendation can be performed according to different browsing preferences of different users.

In the embodiment of the invention, the browsing preference of the user is, for example, a certain borrowed product, a tendency interest rate, a product period and the like.

In the embodiment of the present invention, the analyzing the behavior of the user according to the big data batching service to obtain the browsing preference of the user includes: counting the occurrence frequency of the user behavior corresponding to each data in the user behavior record table by using big data batch service; and determining the user behavior exceeding the preset frequency value as the browsing preference of the user.

In detail, the configuring the attribute of the main push product according to the browsing preference of the user, and establishing a main push product attribute table according to the attribute of the main push product comprises: acquiring preference participles of browsing preference of a user, and converting the preference participles into preference vectors; obtaining product participles with preset product attributes, and converting the product participles into product vectors; calculating the matching degree of the preference vector and the product vector by using a matching degree calculation formula; selecting products corresponding to the product vectors exceeding the preset matching degree as main pushing products; and filling product attributes corresponding to the main pushing product into a preset third blank table, and naming the third blank table as a main pushing product attribute table.

In detail, the calculating the matching degree of the preference vector and the product vector by using the matching degree calculation formula includes:

calculating the matching degree of the preference vector and the product vector by using the following matching degree calculation formula:

wherein, P is the matching degree, alpha is a preference vector converted from the browsing preference of any user, and beta is a product vector converted from the product attribute.

Specifically, because the preference of the user is unstable, the preference of the same user can change along with the change of time and the change of product replacement, so that the behavior data of the user can be analyzed once every preset time by using the big data batch service, and the preference content of the current user can be obtained according to the behavior data of the user at the time, so that automatic recommendation can be performed according to the content.

And S4, selecting a recommended product from a preset product table according to the main push product attribute table, filling the recommended product into a preset recommendation table, determining the recommendation table as a user automatic recommendation table, and pushing the recommended product in the user automatic recommendation table to a user mobile phone APP.

Because different products may correspond to the same product attribute, when pushing is performed by a user, all existing products with the same attribute need to be pushed, so that more choices are provided for the user, and therefore all products corresponding to a single attribute need to be found out.

In an embodiment of the present invention, the selecting a recommended product from a preset product table according to the master push product attribute table, filling the recommended product into a preset recommended table, establishing the recommended table as an automatic user recommendation table, and pushing the recommended product in the automatic user recommendation table to a user mobile phone APP includes: establishing a main push product decision tree model according to a main push product attribute table; inputting the attributes of the main push products into the decision tree model one by one; summarizing the product records obtained each time in a preset recommendation table, and deleting redundant same products in the recommendation table; and the recommendation table is the user recommendation table, and the establishment of the user automatic recommendation table is completed.

In detail, the main push product decision tree model is established according to a main push product attribute table, wherein the mathematical expression of the decision tree model is as follows:

wherein g (x) is an output value of the decision tree, x is a parameter of the decision tree, and f (y) is an input value of the decision function; and taking the attributes of the main push products as input values of a decision tree, and calculating and outputting the product labels of the main push products through the decision tree model.

And when the product label is alpha, the product corresponding to the product label is summarized under the attribute of the main push product, namely the main push product corresponding to the attribute of the main push product comprises the product corresponding to the product label alpha.

When the product label is beta, the product corresponding to the product label cannot be summarized under the attribute of the main push product, that is, the main push product corresponding to the attribute of the main push product does not contain the product corresponding to the product label beta.

In the embodiment of the invention, the recommended products in the user automatic recommendation table are pushed to the user mobile phone APP, for example, when a user opens a home page of the APP, the products are randomly selected from the user automatic recommendation table for recommendation by using the buffer loading time of the entering page, or a small block of special user product recommendation is separately marked on the home page of the user, the products to be pushed are played in a rolling way, and the user is attracted to click, inquire and purchase.

In addition, the method for searching the user recommended products by using the decision tree model has high efficiency and low repetition rate, can accurately push the products, and better realizes the automatic recommendation based on the user behavior configuration.

Fig. 4 is a functional block diagram of a product recommendation device configured based on user behavior according to an embodiment of the present invention.

The product recommendation device 100 configured based on user behavior according to the present invention can be installed in an electronic device. According to the realized functions, the product recommendation device 100 configured based on the user behavior may include a behavior recording module 101, a building service module 102, a table building module 103, and an automatic pushing module 104. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.

In the present embodiment, the functions regarding the respective modules/units are as follows:

the behavior recording module 101: acquiring a historical browsing record and browsing duration of a user mobile phone APP by using a preset embedded point service, and generating a user behavior record table according to the historical browsing record and the browsing duration;

the setup service module 102: configuring a batch running factor by using a preset background configuration system, constructing a batch running configuration table according to the batch running factor, and constructing a big data batch running service according to the batch running configuration table;

the build table module 103: analyzing the behavior of the user according to the big data batch service to obtain the browsing preference of the user, configuring the attribute of a main push product according to the browsing preference of the user, and establishing a main push product attribute table according to the attribute of the main push product;

the auto-push module 104: and selecting a recommended product from a preset product table according to the main push product attribute table, filling the recommended product into a preset recommendation table, determining the recommendation table as a user automatic recommendation table, and pushing the recommended product in the user automatic recommendation table to a user mobile phone APP.

In detail, when the modules in the product recommendation device 100 configured based on user behavior in the embodiment of the present invention are used, the same technical means as the product recommendation method configured based on user behavior described in fig. 1 to fig. 3 are adopted, and the same technical effects can be produced, which is not described herein again.

Fig. 5 is a schematic structural diagram of an electronic device for implementing a product recommendation method configured based on user behavior according to an embodiment of the present invention.

The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a product recommendation program configured based on user behavior, stored in the memory 11 and executable on the processor 10.

In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules stored in the memory 11 (for example, executing a product recommendation program configured based on user behavior, etc.), and calling data stored in the memory 11.

The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used to store not only application software installed in the electronic device and various types of data, such as codes of a product recommendation program configured based on user behavior, but also data that has been output or is to be output temporarily.

The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.

The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit, such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.

Only electronic devices having components are shown, and those skilled in the art will appreciate that the structures shown in the figures do not constitute limitations on the electronic devices, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.

For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

The product recommendation program configured based on user behavior stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, and when executed in the processor 10, can realize:

Specifically, the specific implementation method of the instruction by the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to the drawings, which is not described herein again.

Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).

The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:

and selecting a recommended product from a preset product table according to the main push product attribute table, filling the recommended product into a preset recommendation table, determining the recommendation table as a user automatic recommendation table, and pushing the recommended product in the user automatic recommendation table to a user mobile phone APP.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention 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, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.

The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A product recommendation method based on user behavior configuration is characterized by comprising the following steps:

2. The product recommendation method based on user behavior configuration as claimed in claim 1, wherein the obtaining of the historical browsing record and browsing duration of the user's mobile phone APP by using the preset embedded point service and the generating of the user behavior record table according to the historical browsing record and browsing duration comprises:

acquiring a preset buried point service;

3. The user behavior configuration-based product recommendation method of claim 2, wherein the obtaining of the preset buried point service comprises:

acquiring a corresponding embedded point element of a preset embedded point demand in a user mobile phone;

acquiring an information API of the embedded point element;

4. The product recommendation method based on user behavior configuration as claimed in claim 1, wherein the building of the batch configuration table according to the batch factor and the building of the big data batch service according to the batch configuration table comprises:

filling the configuration factor and the factor data into a preset second blank table to obtain a batch running configuration table;

5. The method of claim 1, wherein analyzing the user's behavior according to the big data batching service to obtain the user's browsing preferences, comprises:

6. The method for recommending products based on user behavior configuration according to claim 1, wherein said configuring attributes of push-home products according to browsing preferences of users and building a push-home product attribute table according to the attributes of the push-home products comprises:

7. The user behavior configuration-based product recommendation method according to any one of claims 1 to 6, wherein the selecting a recommended product from a preset product table according to the master product attribute table and filling the recommended product into a preset recommendation table, and establishing the recommendation table as a user automatic recommendation table comprises:

8. An apparatus for configuring product recommendations based on user behavior, the apparatus comprising:

an automatic pushing module: and selecting a recommended product from a preset product table according to the main push product attribute table, filling the recommended product into a preset recommendation table, determining the recommendation table as a user automatic recommendation table, and pushing the recommended product in the user automatic recommendation table to a user mobile phone APP.

9. An electronic device, characterized in that the electronic device comprises:

at least one processor;

and a memory communicatively coupled to the at least one processor;

wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the user behavior-based configured product recommendation method of any of claims 1-7.

10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the user behavior profiling-based product recommendation method according to any one of claims 1 to 7.