WO2020124962A1

WO2020124962A1 - Product recommendation method and apparatus based on data analysis and terminal device

Info

Publication number: WO2020124962A1
Application number: PCT/CN2019/090831
Authority: WO
Inventors: 乐志能
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-12-19
Filing date: 2019-06-12
Publication date: 2020-06-25
Also published as: CN109785049A

Abstract

The present application relates to the technical field of big data, and disclosed are a product recommendation method and apparatus based on data analysis and a terminal device. The method comprises: when detecting a product recommendation request inputted by a user, obtaining a first product list according to the indication of the product recommendation request; determining a second product list from a general service scene logic by analyzing historical data of the user; removing products, belonging to the second product list, in the first product list from the first product list to obtain a third product list; and performing recommendation processing on the products in the third product list. By implementing the method, the number of recommended products can be reduced on the basis of ensuring the optionality of the recommended products, and the user experience is improved.

Description

Product recommendation method, device and terminal equipment based on data analysis

Technical field

This application requires a Chinese patent application CN filed on December 19, 2018 with the invention titled "A product recommendation method, device and terminal equipment based on data analysis" The priority of 201811556324.8 is hereby incorporated by reference.

The present disclosure relates to the field of big data technology, and in particular, to a product recommendation method, device, and terminal device based on data analysis.

Background technique

Most online shopping platforms have a product recommendation function, which can provide users with recommended products that meet the filtering conditions based on the filtering conditions entered by the user.

However, the inventor realized that due to the large number of products on the online shopping platform, the number of recommended products provided to users based on the screening conditions is also large, which results in users taking a long time to select their favorite products from the recommended products and affecting the user experience.

technical problem

In order to solve the problems in the related art, the present disclosure provides a product recommendation method, device and terminal device based on data analysis.

Technical solution

A product recommendation method based on data analysis, the method includes:

When a product recommendation request input by the user is detected, the first product list is obtained according to the instruction of the product recommendation request;

The second product list is determined from the general service scenario logic by analyzing the user's historical data; wherein, the general service scenario logic includes the product serial number and the preset service scenario logic corresponding to the product serial number, the preset service The scene logic is used to indicate that the product indicated by the corresponding product serial number is not recommended;

Removing products belonging to the second product list from the first product list from the first product list to obtain a third product list;

Recommend the products in the third product list.

A product recommendation device based on data analysis, including:

An obtaining unit, configured to obtain a first product list according to the instruction of the product recommendation request when a product recommendation request input by the user is detected;

A determining unit, configured to determine a second product list from the general service scenario logic by analyzing historical data of the user; wherein, the general service scenario logic includes a product serial number and a preset service scenario logic corresponding to the product serial number, The preset service scenario logic is used to indicate that the product indicated by the corresponding product serial number is not recommended for processing;

A processing unit, configured to exclude products belonging to the second product list from the first product list from the first product list to obtain a third product list;

The recommendation unit is used for recommending the products in the third product list.

A computer non-volatile readable storage medium that stores a computer program that causes a computer to perform a product recommendation method based on data analysis as described above.

A terminal device, the terminal device includes:

processor;

A memory, in which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, implement the product recommendation method based on data analysis as described above.

Beneficial effect

The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: In this method, the recommended products (products in the first product list) are further filtered by combining the user's historical data and the general service scenario logic (the first Products in the three-product list), which can ensure the selectivity of recommended products, and at the same time, the number of products in the third product list is usually less than the number of products in the first product list, so it also achieves a reduction in the number of recommended products the goal of.

It should be understood that the above general description and the following detailed description are only exemplary and do not limit the present disclosure.

BRIEF DESCRIPTION

The drawings herein are incorporated into the specification and constitute a part of the specification, show embodiments consistent with the application, and are used together with the specification to explain the principles of the application.

Fig. 1 is a schematic diagram of a product recommendation device based on data analysis according to an exemplary embodiment;

Fig. 2 is a flow chart showing a method for product recommendation based on data analysis according to an exemplary embodiment;

Fig. 3 is a flow chart showing a method for product recommendation based on data analysis according to another exemplary embodiment;

Fig. 4 is a block diagram of a product recommendation device based on data analysis according to an exemplary embodiment.

Embodiments of the invention

The exemplary embodiments will be explained in detail here, examples of which are shown in the drawings. When referring to the drawings below, unless otherwise indicated, the same numerals in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of devices and methods consistent with some aspects of the application as detailed in the appended claims.

The implementation environment of the present invention may be a portable terminal device, such as a smart phone, a tablet computer, or a desktop computer. Fig. 1 is a schematic diagram of a product recommendation device based on data analysis according to an exemplary embodiment. The product recommendation device 100 based on data analysis may be the aforementioned portable terminal device. As shown in FIG. 1, the product recommendation device 100 based on data analysis may include one or more of the following components: a processing component 102, a memory 104, a power component 106, a multimedia component 108, an audio component 110, a sensor component 114 and a communication component 116.

The processing component 102 generally controls the overall operations of the product recommendation device 100 based on data analysis, such as operations associated with display, phone calls, data communication, camera operations, and recording operations. The processing component 102 may include one or more processors 118 to execute instructions to complete all or part of the steps of the method described below. In addition, the processing component 102 may include one or more modules for facilitating interaction between the processing component 102 and other components. For example, the processing component 102 may include a multimedia module to facilitate interaction between the multimedia component 108 and the processing component 102.

The memory 104 is configured to store various types of data to support the operation of the product recommendation device 100 based on data analysis. Examples of these data include instructions for any application program or method operating on the product recommendation device 100 based on data analysis. The memory 104 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (Static Random Access Memory (SRAM for short), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The memory 104 further stores one or more modules for the one or more modules to be configured to be executed by the one or more processors 118 to complete all or part of the steps in the method shown below.

The power supply component 106 supplies power to various components of the product recommendation device 100 based on data analysis. The power component 106 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the product recommendation device 100 based on data analysis.

The multimedia component 108 includes a screen that provides an output interface between the product recommendation device 100 based on data analysis and the user. In some embodiments, the screen may include a liquid crystal display (Liquid Crystal Display, referred to as LCD) and touch panel. If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or sliding action, but also detect the duration and pressure related to the touch or sliding operation. The screen may also include an organic electroluminescence display (Organic Light Emitting Display, referred to as OLED).

The audio component 110 is configured to output and/or input audio signals. For example, the audio component 110 includes a microphone (Microphone, MIC for short). When the product recommendation device 100 based on data analysis is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in the memory 104 or sent via the communication component 116. In some embodiments, the audio component 110 further includes a speaker for outputting audio signals.

The sensor assembly 114 includes one or more sensors for providing various aspects of status assessment for the product recommendation device 100 based on data analysis. For example, the sensor component 114 can detect the on/off state of the product recommendation device 100 based on data analysis, the relative positioning of the components, and the sensor component 114 can also detect the product recommendation device 100 based on data analysis or the product recommendation device 100 based on data analysis The position of one component changes and the temperature of the product recommendation device 100 based on data analysis. In some embodiments, the sensor assembly 114 may also include a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 116 is configured to facilitate wired or wireless communication between the product recommendation device 100 and other devices based on data analysis. The product recommendation device 100 based on data analysis can access a wireless network based on a communication standard, such as WiFi (Wireless-Fidelity, wireless fidelity). In an exemplary embodiment, the communication component 116 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 116 further includes a Near Field Communication (NFC) module for facilitating short-range communication. For example, the NFC module can be based on radio frequency identification (Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth technology and other technologies.

In an exemplary embodiment, the product recommendation device 100 based on data analysis may be used by one or more application specific integrated circuits (Application Specific Integrated Circuit (ASIC for short), digital signal processor, digital signal processing equipment, programmable logic device, field programmable gate array, controller, microcontroller, microprocessor or other electronic components, used to perform the following method.

Fig. 2 is a flow chart showing a method for product recommendation based on data analysis according to an exemplary embodiment. As shown in Figure 2, this method includes the following steps.

Step 210: When a product recommendation request input by the user is detected, obtain a first product list according to the instruction of the product recommendation request.

In the embodiment of the present invention, the above product recommendation request may include information such as product type and product price range. In an exemplary embodiment, the data analysis-based product recommendation method is applicable to an online shopping platform. The user can log in to the shopping platform through a user account. Before performing step 210, it can also detect whether the user successfully logs in to the online shopping platform, and When it is detected that the user has successfully logged in, the user information associated with the user account is obtained, and the user label is obtained by analyzing the user information. When the product recommendation request input by the user is detected, acquiring the first product list according to the instruction of the product recommendation request may include: when detecting the product recommendation request input by the user, acquiring according to the instruction of the product recommendation request and the user tag The first product list. By implementing this method, when a user successfully logs in to the online shopping platform and enters a product recommendation request, the first product list is obtained according to the instruction of the product recommendation request and the user tag, and the matching degree between the products in the first product list and the user can be improved.

Optionally, in the implementation of the present invention, the method mentioned in step 210 for obtaining the above-mentioned first product list may also be that, when it is detected that the user successfully logs in to the online shopping platform, the user's last login to the online shopping platform is obtained. Record and obtain products associated with the browsing record to generate a first product list.

Step 220: Determine the second product list from the general service scenario logic by analyzing the user's historical data; wherein, the general service scenario logic includes the product serial number and the preset service scenario logic corresponding to the product serial number, and the preset service scenario logic Used to indicate that the product indicated by the corresponding product serial number is not recommended.

 In an exemplary embodiment, determining the second product list from the general service scenario logic by analyzing the user's historical data may include:

Obtain the recommended times and clicks of historically recommended products by analyzing the user's historical data;

Find the preset service scene logic corresponding to the historical recommended products from the general service scene logic;

The second product list is determined from the historical recommended products according to the recommended times and clicks of the historical recommended products and the logic of the preset service scene corresponding to the historical recommended products.

Step 230: Remove the products belonging to the second product list from the first product list to obtain the third product list.

Step 240: Perform recommendation processing on the products in the third product list.

The following is an example for steps 210 to 240. Suppose that the products in the first product list include product a and product b. In the general service scenario logic, the preset service scenario logic corresponding to product a is recommended three times and the user has not Click to no longer recommend to the user, the default service scenario logic corresponding to product b is to recommend 5 times and the user has not clicked, no longer recommend to the user, by analyzing the user's historical data, it is obtained that product a has been recommended to the user 3 times and the user has not clicked Product b has also been recommended to the user 5 times and the user has not clicked it. At this time, it is necessary to do not recommend product a and product b in the above first product list, that is, to remove product a and product b from the above first product list .

It should be noted that the above general service scenario logic is obtained by counting the behavior data of a preset number of users on the product within the second preset duration. Among them, the number of recommendations for each product is the preset number of recommendations, and the behavior data for the product can specifically include data such as whether to click on the product, click on the number of recommendations, whether to buy, and purchase on the number of recommendations. The user comprehensively evaluates the behavior data of the same product to obtain the logic of the preset service scenario corresponding to the product.

By performing steps 210 to 230, the recommended products can be further screened to achieve the purpose of reducing the number of recommended products on the basis of ensuring the selectivity of the recommended products, and improving the user's experience.

In another exemplary embodiment, recommending the products in the third product list may include:

Control the product recommendation window to output the products in the third product list in a polling manner according to preset rules. If the above first product list is obtained by obtaining the user's browsing history of the user's last login on the online shopping platform, further optional, the product recommendation window may automatically pop up after detecting that the user successfully logs in to the online shopping platform, and execute the above Control the product recommendation window to output the products in the third product list in a polling manner according to the preset rules, and when the user's click operation on the product recommendation window is not detected within the third preset duration, the product recommendation window is terminated Output. It should be noted that the third preset duration can be obtained by counting historical operation data of the user on the product recommendation window. By implementing this method, users can be provided with an intelligent product recommendation method. In addition, when the user’s click operation on the product recommendation window is not detected within the third preset time period, the output of the product recommendation window is terminated, which can be reduced Interference with users.

Before controlling the product recommendation window to output the products in the third product list in a polling manner according to the preset rules, the user information associated with the user account can also be obtained. The user information includes at least the user's age, gender, and occupation; The target window skin matching the user information is determined from the preset window skins; the window skin of the product recommendation window is set as the target window skin. By implementing this exemplary embodiment, if the products in the third product list are output in a polling manner according to preset rules in the product recommendation window, the window skin of the product recommendation window can be set as the target matching the user information according to the user information Window skins improve fun and further enhance the user's experience.

Optionally, in an exemplary embodiment, controlling the product recommendation window to output the products in the third product list in a polling manner according to a preset rule may include:

Obtain the average page-changing time of the user recommendation window for the product within the first preset time period;

Set the above average page change duration to the polling period of the product recommendation window;

According to the preset rules and the products in the third product list, set the number of polling pages in the product recommendation window and the products included in each polling page;

The control product recommendation window polls the output polling page in accordance with the above polling cycle.

By implementing this optional implementation method, the polling period of the product recommendation window can be set according to the average page-changing time of the user for the product recommendation window to provide the user with an automatic polling method applicable.

Fig. 3 is a flow chart showing a method for product recommendation based on data analysis according to another exemplary embodiment. As shown in FIG. 3, in addition to steps 210 to 240 shown in FIG. 2, the product recommendation method based on data analysis in this embodiment also includes the following steps:

For the detailed description of steps 310 to 340, please refer to the description of steps 210 to 240 in the product recommendation method based on data analysis shown in FIG. 2, which will not be repeated in this embodiment of the present invention.

Step 350, it is detected whether there is a click operation on the product in the third product list, and if so, step 360 to step 370 are performed; if not, the process ends.

Step 360, the product corresponding to the above click operation is regarded as the product to be introduced.

Step 370: Control the output of the acquired introduction information of the product to be introduced according to a preset output mode.

Performing steps 350 to 370, when there is a click operation on the product in the third product list, the introduction information of the product corresponding to the click operation may be output to the user.

Optionally, in an exemplary embodiment, after step 360 is performed, and before step 370 is performed, a camera module is used to obtain a user image; by identifying the obtained user image, the preset introduction associated with the product to be introduced is introduced The target introduction information is determined in the information. Then, the introduction of the introduction information of the product to be introduced obtained by the above control according to the preset output mode may include: determining the target output mode matching the target introduction information from the preset output mode; controlling the target introduction information to be output in the target output mode . By implementing this exemplary embodiment, it is possible to output the target introduction information conforming to the user in a target output mode by identifying the user image, which makes the target introduction information more readable, which is beneficial to improve the product conversion rate of the recommended products.

It should be noted that the user's age and gender can be obtained by identifying the user image, and the preset introduction information associated with the product to be introduced can correspond to the user's age and gender, and the specific corresponding rule can be the preset associated with the product to be introduced Each preset introduction information in the introduction information may correspond to a preset age group and a gender, and the target introduction information is preset introduction information corresponding to the user's age and gender.

Wherein, the above-mentioned preset output mode may be text, animation or voice, which is not limited in the embodiment of the present invention, and each preset introduction information corresponds to one output mode.

The following is an embodiment of a product recommendation device based on data analysis disclosed in the present invention.

Fig. 4 is a block diagram of a product recommendation device based on data analysis according to an exemplary embodiment. As shown in FIG. 4, the product recommendation device based on data analysis may include:

The obtaining unit 401 is configured to obtain the first product list according to the instruction of the product recommendation request when the product recommendation request input by the user is detected.

The obtaining unit 401 can also be used to detect whether the user successfully logs in to the online shopping platform before obtaining the first product list according to the instruction of the product recommendation request when a product recommendation request input by the user is detected, and when it is detected that the user successfully logs in To obtain user information associated with the user account, and obtain user tags by analyzing the user information. Based on this method, the obtaining unit 401 is used to obtain the first product list according to the instruction of the product recommendation request when a product recommendation request input by the user is detected. The obtaining unit 401 may be used to detect when a user input is detected. When requesting a product recommendation, the first product list is obtained according to the instruction of the product recommendation request and the obtained user tag. By implementing this method, when the user successfully logs in to the online shopping platform and enters a product recommendation request, the first product list is obtained according to the instruction of the product recommendation request and the user tag, and the matching degree between the product and the user in the first product list can be improved.

Optionally, in the implementation of the present invention, the acquisition unit 401 may also acquire the first product list in a manner that when the acquisition unit 401 detects that the user has successfully logged in to the online shopping platform, the acquisition unit 401 obtains the browsing history of the user's last login to the online shopping platform And obtain the product associated with the browsing record to generate a first product list.

The determining unit 402 is configured to determine the second product list from the general service scenario logic by analyzing the user's historical data; wherein, the general service scenario logic includes the product serial number and the preset service scenario logic corresponding to the product serial number, the preset service The scene logic is used to indicate that the product indicated by the corresponding product serial number is not recommended.

In an exemplary embodiment, the determining unit 402 is used to determine the second product list from the general service scenario logic by analyzing the user's historical data, which may specifically be:

The above determination unit 402 is used to obtain the recommended times and clicks of historically recommended products by analyzing the user's historical data; and find the preset service scene logic corresponding to the historically recommended products from the general service scene logic; and the recommendations based on the historically recommended products The number of times and the number of clicks, and the logic of the preset service scene corresponding to the historical recommended product, determine the second product list from the historical recommended products.

For a detailed explanation of the determination unit 402, please refer to the examples of steps 210 to 240 in the product recommendation method based on data analysis shown in FIG. 2, which will not be repeated in this embodiment of the present invention.

The processing unit 403 is configured to exclude products belonging to the second product list from the first product list from the first product list to obtain a third product list.

The recommendation unit 404 is used for recommending the products in the third product list.

In another exemplary embodiment, the manner in which the recommendation unit 404 is used to recommend the products in the third product list may specifically be: the above recommendation unit 404 is used to control the product recommendation window in a polling manner according to preset rules The products in the third product list are output. Based on this exemplary embodiment, the above recommendation unit 404 may also be used to control the product recommendation window to obtain user information associated with a user account before outputting products in the third product list in a polling manner according to a preset rule, the user information being at least Including the user's age, gender and occupation; and determining the target window skin matching the user information from the preset window skin according to the user information; and setting the window skin of the product recommendation window as the target window skin.

Further optionally, the above recommendation unit 404 is used to control the product recommendation window to output the products in the third product list in a polling manner according to preset rules. The recommendation unit 404 may be used to obtain the first preset duration The average page-changing time of the product recommendation window for internal users; and setting the above average page-changing time as the polling period of the product recommendation window; and setting the polling of the product recommendation window based on preset rules and products in the third product list The number of pages and the products included in each polling page; and the control product recommendation window polls the polling output page in accordance with the above polling cycle.

In an embodiment, the determining unit 402 includes:

The first obtaining subunit 4021 is used to obtain the recommended times and clicks of historically recommended products by analyzing the user's historical data;

The searching subunit 4022 is used to search for the preset service scenario logic corresponding to the historical recommended product from the general service scenario logic;

The first determining subunit 4023 is configured to determine a second product list from the historical recommended products according to the recommended times and clicks of the historical recommended products and the logic of the preset service scene corresponding to the historical recommended products.

In an embodiment, the recommendation unit 404 includes:

The first output subunit 4044 is used to control the product recommendation window to output the products in the third product list in a polling manner according to a preset rule.

In an embodiment, the device further includes:

The second obtaining subunit 4041 is configured to obtain user information associated with the user account, where the user information includes at least the user's age, gender, and occupation;

The second determining subunit 4042 is configured to determine a target window skin matching the user information from preset window skins according to the user information;

The setting subunit 4043 is used to set the window skin of the product recommendation window as the target window skin.

In an embodiment, the first output subunit 4044 includes:

The obtaining module 4044-1 is used to obtain the average page-changing time of the user for the product recommendation window within the first preset time period;

The first setting module 4044-2 is configured to set the average page-changing duration as the polling period of the product recommendation window;

The second setting module 4044-3 is configured to set the number of polling pages of the product recommendation window and the products included in the polling page according to preset rules and products in the third product list;

The output module 4044-4 is configured to control the product recommendation window to poll and output the polling page according to the polling cycle.

In an embodiment, the device further includes:

The detection unit 407 is configured to detect whether there is a click operation on a product in the third product list;

The confirmation unit 408 is configured to, when there is the click operation, use the product corresponding to the click operation as the product to be introduced;

The output unit 411 is configured to control the obtained introduction information of the product to be introduced to be output according to a preset output mode.

In an embodiment, the device further includes:

The user image obtaining unit 409 is used to obtain a user image by using a camera module;

The identification unit 410 is configured to determine target introduction information from the preset introduction information associated with the product to be introduced by identifying the user image;

The output unit 411 includes:

The third determining subunit 4111 is configured to determine a target output mode matching the target introduction information from preset output modes;

The second output subunit 4112 is configured to control the target introduction information to be output in the target output mode.

The invention also provides a terminal device. The terminal device includes:

processor;

A memory, which stores computer readable instructions. When the computer readable instructions are executed by the processor, the product recommendation method based on data analysis as described above is implemented.

The terminal device may be the product recommendation device 100 based on data analysis shown in FIG. 1.

In an exemplary embodiment, the present invention also provides a computer non-volatile readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the data analysis-based Product recommendation method.

It should be understood that the present disclosure is not limited to the precise structure that has been described above and shown in the drawings, and that various modifications and changes can be performed without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

A product recommendation method based on data analysis, characterized in that the method includes:

When a product recommendation request input by the user is detected, the first product list is obtained according to the instruction of the product recommendation request;

The second product list is determined from the general service scenario logic by analyzing the user's historical data; wherein, the general service scenario logic includes the product serial number and the preset service scenario logic corresponding to the product serial number, the preset service The scene logic is used to indicate that the product indicated by the corresponding product serial number is not recommended;

Removing products belonging to the second product list from the first product list from the first product list to obtain a third product list;

Recommend the products in the third product list.
The method according to claim 1, wherein determining the second product list from the general service scenario logic by analyzing the user's historical data includes:

Obtain the recommended times and clicks of historically recommended products by analyzing the user's historical data;

Searching for the preset service scene logic corresponding to the historical recommended product from the general service scene logic;

The second product list is determined from the historical recommended products according to the recommended times and click times of the historical recommended products, and the preset service scene logic corresponding to the historical recommended products.
The method according to claim 1 or 2, wherein the recommendation processing for the products in the third product list includes:

Control the product recommendation window to output the products in the third product list in a polling manner according to preset rules.
The method according to claim 3, wherein before the control product recommendation window outputs the products in the third product list in a polling manner according to a preset rule, the method further comprises:

Obtain user information associated with a user account, where the user information includes at least the user's age, gender, and occupation;

Determine a target window skin matching the user information from preset window skins according to the user information;

The window skin of the product recommendation window is set as the target window skin.
The method according to claim 3, wherein the control product recommendation window outputs the products in the third product list in a polling manner according to a preset rule, including:

Obtain the average page-changing time of the user recommendation window for the product within the first preset time period;

Setting the average page-changing duration to the polling period of the product recommendation window;

Set the number of polling pages in the product recommendation window and the products included in the polling page according to preset rules and products in the third product list;

Controlling the product recommendation window to poll and output the polling page according to the polling cycle.
The method according to claim 1, wherein after the recommendation processing is performed on the products in the third product list, the method further comprises:

Detecting whether there is a click operation on the product in the third product list;

When the click operation exists, the product corresponding to the click operation is taken as the product to be introduced;

Control the obtained introduction information of the product to be introduced according to a preset output mode.
The method according to claim 6, wherein when the click operation exists, the product corresponding to the click operation is taken as the product to be introduced, and the product of the product to be introduced obtained by the control Before the introduction information is output according to the preset output method, the method further includes:

Use the camera module to obtain user images;

Determine the target introduction information from the preset introduction information associated with the product to be introduced by identifying the user image;

The introduction information of the product to be introduced obtained by the control is output according to a preset output mode, including:

Determining a target output mode matching the target introduction information from the preset output modes;

The target introduction information is controlled to be output in the target output mode.
A product recommendation device based on data analysis is characterized by including:

An obtaining unit, configured to obtain a first product list according to the instruction of the product recommendation request when a product recommendation request input by the user is detected;

A determining unit, configured to determine a second product list from the general service scenario logic by analyzing historical data of the user; wherein, the general service scenario logic includes a product serial number and a preset service scenario logic corresponding to the product serial number, The preset service scenario logic is used to indicate that the product indicated by the corresponding product serial number is not recommended for processing;

A processing unit, configured to exclude products belonging to the second product list from the first product list from the first product list to obtain a third product list;

The recommendation unit is used for recommending the products in the third product list.
The apparatus according to claim 8, wherein the determining unit comprises:

The first obtaining subunit is used to obtain the recommended times and clicks of historically recommended products by analyzing the user's historical data;

A search subunit, used to find the preset service scene logic corresponding to the historical recommended product from the general service scene logic;

The first determining subunit is configured to determine a second product list from the historical recommended products according to the recommended times and clicks of the historical recommended products and the logic of the preset service scene corresponding to the historical recommended products.
The device according to claim 8 or 9, wherein the recommendation unit comprises:

The first output subunit is used to control the product recommendation window to output the products in the third product list in a polling manner according to a preset rule.
The device according to claim 10, further comprising:

The second obtaining subunit is used to obtain user information associated with the user account, where the user information includes at least the user's age, gender, and occupation;

A second determining subunit, configured to determine a target window skin matching the user information from preset window skins according to the user information;

A setting subunit is used to set the window skin of the product recommendation window as the target window skin.
The apparatus according to claim 10, wherein the first output subunit comprises:

An obtaining module, configured to obtain the average page-changing time of the user's product recommendation window within the first preset time period;

A first setting module, configured to set the average page-breaking time as the polling period of the product recommendation window;

A second setting module, configured to set the number of polling pages of the product recommendation window and the products included in the polling page according to preset rules and products in the third product list;

The output module is configured to control the product recommendation window to poll and output the polling page according to the polling cycle.
The device according to claim 8, further comprising:

A detection unit, configured to detect whether there is a click operation on a product in the third product list;

The confirmation unit is configured to use the product corresponding to the click operation as the product to be introduced when the click operation exists;

The output unit is used to control the obtained introduction information of the product to be introduced to be output according to a preset output mode.
The device according to claim 13, further comprising:

User image acquisition unit, used to obtain user images using camera modules;

A recognition unit, configured to determine target introduction information from the preset introduction information associated with the product to be introduced by identifying the user image;

The output unit includes:

A third determining subunit, configured to determine a target output mode matching the target introduction information from preset output modes;

The second output subunit is used to control the target introduction information to be output in the target output mode.
A computer non-volatile readable storage medium, characterized in that it stores a computer program, which when executed by a processor causes the processor to perform the following processing:

When a product recommendation request input by the user is detected, the first product list is obtained according to the instruction of the product recommendation request;

The second product list is determined from the general service scenario logic by analyzing the user's historical data; wherein, the general service scenario logic includes the product serial number and the preset service scenario logic corresponding to the product serial number, the preset service The scene logic is used to indicate that the product indicated by the corresponding product serial number is not recommended;

Removing products belonging to the second product list from the first product list from the first product list to obtain a third product list;

Recommend the products in the third product list.
The storage medium according to claim 15, wherein the computer program, when executed by the processor, causes the processor to execute when the second product list is determined from the general service scenario logic by analyzing the user's historical data Process as follows:

Obtain the recommended times and clicks of historically recommended products by analyzing the user's historical data;

Searching for the preset service scene logic corresponding to the historical recommended product from the general service scene logic;

The second product list is determined from the historical recommended products according to the recommended times and click times of the historical recommended products, and the preset service scene logic corresponding to the historical recommended products.
The storage medium according to claim 15 or 16, wherein the computer program, when executed by the processor, causes the processor to perform the following processing when the recommendation processing is performed on the products in the third product list:

Control the product recommendation window to output the products in the third product list in a polling manner according to preset rules.
The storage medium according to claim 17, wherein when the computer program is executed by the processor, the processor causes the processor to output the third product list in a polling manner according to a preset rule in the control product recommendation window Of products before the following processing:

Obtain user information associated with a user account, where the user information includes at least the user's age, gender, and occupation;

Determine a target window skin matching the user information from preset window skins according to the user information;

The window skin of the product recommendation window is set as the target window skin.
The storage medium according to claim 17, wherein when the computer program is executed by the processor, the processor causes the processor to output the third product list in a polling manner according to a preset rule in the control product recommendation window The product is executed as follows:

Obtain the average page-changing time of the user recommendation window for the product within the first preset time period;

Setting the average page-changing duration to the polling period of the product recommendation window;

Set the number of polling pages of the product recommendation window and the products included in the polling page according to preset rules and products in the third product list;

Controlling the product recommendation window to poll and output the polling page according to the polling cycle.
The storage medium according to claim 15, wherein the computer program, when executed by the processor, causes the processor to perform the following processing after the recommendation processing of the products in the third product list:

Detecting whether there is a click operation on the product in the third product list;

When the click operation exists, the product corresponding to the click operation is taken as the product to be introduced;

Control the obtained introduction information of the product to be introduced according to a preset output mode.
The storage medium according to claim 20, wherein the computer program, when executed by the processor, causes the processor to use the product corresponding to the click operation as the product to be introduced when the click operation exists After that, and before the introduction information of the product to be introduced obtained by the control is output according to the preset output mode, the following processing is performed:

Use the camera module to obtain user images;

Determine the target introduction information from the preset introduction information associated with the product to be introduced by identifying the user image;

The introduction information of the product to be introduced obtained by the control is output according to a preset output mode, including:

Determining a target output mode matching the target introduction information from the preset output modes;

The target introduction information is controlled to be output in the target output mode.
A terminal device, characterized in that the terminal device includes:

processor;

A memory, on which computer-readable instructions are stored, which when executed by the processor causes the processor to perform the following processing:

When a product recommendation request input by the user is detected, the first product list is obtained according to the instruction of the product recommendation request;

The second product list is determined from the general service scenario logic by analyzing the user's historical data; wherein, the general service scenario logic includes the product serial number and the preset service scenario logic corresponding to the product serial number, the preset service The scene logic is used to indicate that the product indicated by the corresponding product serial number is not recommended;

Removing products belonging to the second product list from the first product list from the first product list to obtain a third product list;

Recommend the products in the third product list.
The terminal device according to claim 22, wherein when the computer-readable instructions are executed by the processor, the processor determines the second product from the general service scenario logic by analyzing historical data of the user Perform the following processing when listing:

Obtain the recommended times and clicks of historically recommended products by analyzing the user's historical data;

Searching for the preset service scene logic corresponding to the historical recommended product from the general service scene logic;

The second product list is determined from the historical recommended products according to the recommended times and click times of the historical recommended products, and the preset service scene logic corresponding to the historical recommended products.
The terminal device according to claim 22 or 23, characterized in that, when the computer-readable instructions are executed by the processor, the processor is executed when the recommendation processing for the products in the third product list is performed Process as follows:

Control the product recommendation window to output the products in the third product list in a polling manner according to preset rules.
The terminal device according to claim 24, wherein when the computer-readable instructions are executed by the processor, the processor causes the processor to output the third in a polling manner according to a preset rule in the control product recommendation window The products in the product list perform the following processing before:

Obtain user information associated with a user account, where the user information includes at least the user's age, gender, and occupation;

Determine a target window skin matching the user information from preset window skins according to the user information;

The window skin of the product recommendation window is set as the target window skin.
The terminal device according to claim 24, wherein when the computer-readable instructions are executed by the processor, the processor causes the processor to output the third in a polling manner according to a preset rule in the control product recommendation window The products in the product list are processed as follows:

Obtain the average page-changing time of the user recommendation window for the product within the first preset time period;

Setting the average page-changing duration to the polling period of the product recommendation window;

Set the number of polling pages of the product recommendation window and the products included in the polling page according to preset rules and products in the third product list;

Controlling the product recommendation window to poll and output the polling page according to the polling cycle.
The terminal device according to claim 22, wherein when the computer-readable instructions are executed by the processor, the processor causes the processor to perform the following processing after the recommendation processing of the products in the third product list :

Detecting whether there is a click operation on the product in the third product list;

When the click operation exists, the product corresponding to the click operation is taken as the product to be introduced;

Control the obtained introduction information of the product to be introduced according to a preset output mode.
The terminal device according to claim 27, wherein when the computer-readable instructions are executed by the processor, the processor makes the product corresponding to the click operation as the product corresponding to the click operation when the click operation exists After the product to be introduced, and before the introduction information of the product to be introduced obtained by the control is output according to the preset output mode, the following processing is performed:

Use the camera module to obtain user images;

Determine the target introduction information from the preset introduction information associated with the product to be introduced by identifying the user image;

The introduction information of the product to be introduced obtained by the control is output according to a preset output mode, including:

Determining a target output mode matching the target introduction information from the preset output modes;

The target introduction information is controlled to be output in the target output mode.