Investigation and promotion method and system based on shared information

Abstract

This application relates to the technical field of product research and promotion, disclosing a method and system for research and promotion based on shared information. The method comprises the following steps: obtaining user session shared information; identifying user behavior patterns based on the session shared information, specifically identifying the user's browsing and purchasing behavior patterns; obtaining the user's high-intent products based on the browsing behavior patterns; obtaining the user's associated intended products based on the purchasing behavior patterns; and promoting products to the user based on the high-intent products and associated intended products. This application can provide in-depth user behavior analysis, thereby improving the accuracy of product recommendations and facilitating the optimization of product promotion strategies.

Description

Technical Field

[0001] The present application relates to the technical field of product investigation and promotion, and specifically to a method and system for investigation and promotion based on shared information. Background Art

[0002] By analyzing users' purchase histories and browsing behaviors on the platform, e-commerce platforms can provide personalized recommendations. However, the current industry still has many shortcomings in product research and promotion. While existing data analysis methods can uncover some superficial information, they fall far short of providing a deep understanding of user behavior patterns. When analyzing user browsing and purchasing behaviors, they often only reveal simple associations, struggling to uncover deeper, non-intuitive product connections. Regarding product promotion, current personalized recommendation algorithms still need to be optimized. Existing recommendation systems are mostly based on simple collaborative filtering or content-based recommendation algorithms, which have limitations when dealing with complex user behaviors and diverse product requirements. They often only recommend similar products based on historical user behavior and fail to fully consider users' real-time needs and changing scenarios.

[0003] For example, a Chinese patent with authorization announcement number CN116823382B discloses a product promotion method based on big data, including: determining the product identification of the product to be promoted; determining a target product set based on the product identification, and obtaining a number of target customers corresponding to the target product set; the target product set includes a number of target products related to the product identification of the product to be promoted; obtaining the consumption behavior information of each target customer, and determining the consumption tag of the target customer based on the consumption behavior information; generating promotion information based on the consumption tag and the product parameters of the product to be promoted; and sending the promotion information to the corresponding target customer. The product promotion method provided by this invention can determine the consumption tag of the target customer through the consumption behavior of the target customer and generate promotion information in combination with the product parameters of the product to be promoted. At the same time, since the promotion information is highly consistent with the consumption behavior of the target customer, the product promotion effect is improved.

[0004] For example, Chinese patent application publication number CN106408366A discloses a product promotion method and system, which includes the following steps: H. A user terminal receives a first call instruction from a consumer, which is then transmitted to a service backend, which transmits preliminary product data to the user terminal; after receiving the preliminary data, the user terminal displays it to the consumer; K. The user terminal receives a second call instruction from the consumer, which is then transmitted to the service backend, which transmits production data to the user terminal; after receiving the production data, the user terminal displays it to the consumer. This invention can more comprehensively display information about the product's production process to consumers, improving the comprehensiveness and accuracy of product information and providing valuable reference for consumers.

[0005] The above existing technologies all have the problem raised by this background technology: when analyzing users' browsing and purchasing behaviors, it is difficult to dig out deep, non-intuitive product associations.

[0006] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the application and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the Invention

[0007] The technical problem to be solved by this application is to overcome the defects of the existing technology, provide a survey and promotion method and system based on shared information, provide in-depth user behavior analysis, and thus improve the accuracy of product recommendations.

[0008] To solve the above technical problems, this application provides the following technical solutions:

[0009] In one aspect, the present application provides a survey and promotion method based on shared information, comprising the following steps:

[0010] Get the user's session sharing information;

[0011] Identify user behavior patterns based on the session shared information; specifically, identify user browsing behavior patterns and purchasing behavior patterns;

[0012] Acquire the user's high-intent products based on the browsing behavior pattern; acquire the user's associated-intent products based on the purchasing behavior pattern;

[0013] Product promotion is performed to users based on the high-intent products and related-intent products.

[0014] As a preferred solution of the survey and promotion method based on shared information described in this application, the method of identifying the browsing behavior pattern of users is as follows:

[0015] Extracting each product browsing record of the user for the same type of product from the session shared information;

[0016] Count the types of related pages for the same type of product and assign a number to each type of related page;

[0017] Based on the product browsing records and the numbers of the related pages, a browsing feature sequence and a browsing order matrix are established to record the user's browsing behavior pattern.

[0018] As a preferred solution of the survey and promotion method based on shared information described in the present application, wherein: the browsing feature sequence is used to record the user's attention to each related page; the browsing feature sequence is a vector of length N; where N is the number of related pages for the same type of product; each element in the browsing feature sequence corresponds to a related page;

[0019] The method of establishing a browsing feature sequence is as follows:

[0020] Sort N related pages and record the numbers of the related pages based on the sorting;

[0021] Count the total browsing time of each related page in all product browsing records, and calculate the total browsing time of all related pages;

[0022] Calculate the element value corresponding to each number; combine all element values ​​into a browsing feature sequence; the element value corresponding to any number is the ratio of the total browsing time of the relevant page corresponding to the number to the total browsing time of all relevant pages.

[0023] As a preferred solution of the survey and promotion method based on shared information described in this application, the browsing order matrix is ​​used to record the order in which users browse different related pages. The method for establishing the browsing order matrix is ​​as follows:

[0024] Sort the numbers of N related pages horizontally and vertically to form a browsing order matrix with N rows and N columns, and initialize each element in the browsing order matrix;

[0025] Based on the product browsing history, the user's browsing path and the transition probability of each browsing path are counted; any browsing path includes two related pages, namely the first page and the second page;

[0026] make Indicates that the first page is the i-th related page and the second page is the j-th related page. The browsing path is The transition probability is denoted as , represents the probability that a user browses the jth related page after browsing the ith related page;

[0027] Assign values ​​to the elements in the browsing order matrix based on the transition probability of each browsing path; where the element value in the i-th row and j-th column of the browsing order matrix is .

[0028] As a preferred solution of the survey and promotion method based on shared information described in this application, the identification of the user's browsing behavior pattern also includes dynamically modifying the browsing order matrix, specifically including:

[0029] Identify the user's dependent browsing path; set a transition probability threshold, and mark the browsing path with a transition probability greater than the transition probability threshold as a dependent browsing path;

[0030] If it is detected that the user is browsing the first page of the dependent browsing path, select a page from the related pages not included in the dependent browsing path as the third page; and push the third page to the user through a pop-up window;

[0031] Collect user feedback on pop-up push notifications; the feedback includes explicit positive feedback, implicit positive feedback, and negative feedback;

[0032] The browsing order matrix is ​​modified based on the operation feedback.

[0033] As a preferred embodiment of the survey and promotion method based on shared information described in the present application, if the user jumps to and browses the third page through a pop-up window, the operation feedback is explicit positive feedback; if the user closes the pop-up window and manually jumps to and browses the third page when finishing browsing the first page, the operation feedback is implicit positive feedback; if the user closes the pop-up window and does not jump to the third page when finishing browsing the first page, the operation feedback is negative feedback;

[0034] Modifying the browsing order matrix based on the operation feedback specifically includes:

[0035] Let the first page that depends on the browsing path be the pth related page, the second page that depends on the browsing path be the qth related page, and the third page be the rth related page; if the user's operation feedback is explicit positive feedback, then the browsing path The transition probability Reduce , and browse to the path The transition probability Increase ; is the preset first correction amount; if the user's operation feedback is implicit positive feedback, the browsing path The transition probability Reduce , and browse to the path The transition probability Increase ; is the preset second correction value; if the user's operation feedback is negative feedback, then keep and constant.

[0036] As a preferred solution of the survey and promotion method based on shared information described in this application, the method for identifying the user's purchasing behavior pattern is as follows:

[0037] Extracting the user's product purchase records from the session shared information, and extracting each product purchased by the user from the product purchase records; assigning a product number to each product purchased by the user;

[0038] Record the purchase time and corresponding product number of each product purchased by the user, and establish the user's purchase time series; each position in the purchase time series corresponds to a time point; if the user does not purchase a product at any time point, the position corresponding to the time point is empty; if the user purchases a product at any time point, the element value of the position corresponding to the time point is the product number;

[0039] Setting the length and sliding step of the sliding window; intercepting the purchase time series based on the sliding window and the sliding step; and creating a purchase event based on each intercepted sequence segment;

[0040] A purchase event table is established based on the product number and purchase event to record the user's purchase behavior pattern.

[0041] As a preferred solution of the survey and promotion method based on shared information described in this application, wherein: any column in the purchase event table corresponds to the product number of a product; any row in the purchase event table corresponds to a purchase event;

[0042] The method to create a purchase event table is as follows:

[0043] Create an empty purchase event table based on the number of product numbers and the number of purchase events;

[0044] The empty purchase event table is assigned values ​​based on the product number contained in each purchase event, as follows: in any purchase event, the cell at the corresponding position in the purchase event table for any product purchased by the user is assigned a value of 1, and the cell at the corresponding position in the purchase event table for any product not purchased by the user is assigned a value of 0.

[0045] As a preferred solution of the survey and promotion method based on shared information described in this application, the method for obtaining users' high-intent products based on browsing behavior patterns is as follows:

[0046] Obtain other users' product browsing and purchase records;

[0047] Based on product browsing and purchase records, filter out other users who have browsed and purchased the same type of products and mark them as reference users;

[0048] Establish a browsing feature sequence and browsing order matrix for each reference user;

[0049] For any reference user, calculate the similarity between the browsing feature sequence of the user and the reference user and the similarity between the browsing order matrix and sum them up to get the similarity between the user and the reference user;

[0050] Setting a similarity threshold; marking reference users whose similarity to the user is higher than the similarity threshold as similar users;

[0051] Based on the product purchase records, the specific models of the same type of products purchased by each similar user are extracted; the purchase frequency of each specific model of the product is counted, and the m products with the most purchase frequency are marked as the high-intent products.

[0052] As a preferred solution of the survey and promotion method based on shared information described in this application, the method for obtaining the user's associated intended products based on the purchase behavior pattern is as follows:

[0053] Set relevance threshold and purchase count threshold;

[0054] Combining any two products in the purchase event table whose purchase times are both greater than the purchase times threshold to obtain a first association group; any first association group contains two products with different product numbers;

[0055] Counting the relevance of each first association group, and deleting the first association group whose relevance is less than the relevance threshold; the relevance of the first association group is the number of times the first association group appears in the purchase event table divided by the total number of purchase events;

[0056] Merge any two first association groups and remove duplicates to obtain a second association group; count the association degree of each second association group and delete the second association group whose association degree is less than the association degree threshold; the association degree of the second association group is the number of times the second association group appears in the purchase event table divided by the total number of purchase events; save the first association group and the second association group;

[0057] Extract a list of all products purchased by the user in the last n days based on the product purchase records; n is a positive integer; combine the first association group and the second association group to obtain the user's associated intended products.

[0058] As a preferred solution of the survey and promotion method based on shared information described in this application, wherein: combining the first association group and the second association group to obtain the user's associated intended products specifically includes:

[0059] S101: If there is a second association group containing any two purchased products, and there is a product in the second association group that the user has not purchased, then mark the product in the second association group that the user has not purchased as an association intention product;

[0060] S102: If no associated intended product is obtained in S101, the associated intended product is obtained through the first associated group, as follows:

[0061] If the first association group contains any purchased product, and there is a product in the first association group that the user has not purchased, then the product in the first association group that the user has not purchased is marked as an association intention product;

[0062] All associated intended products are sorted based on the association degrees of the corresponding first associated groups, and the associated intended products corresponding to the M first associated groups with the largest association degrees are retained.

[0063] As a preferred solution of the survey and promotion method based on shared information described in this application, product promotion to users based on the high-intent products and related-intent products includes conventional promotion; the conventional promotion method includes:

[0064] Real-time detection of user browsing and purchasing behavior based on session shared information. When a user is detected to have finished browsing any type of product and no purchases of the same type are detected, the user's high-intent products are obtained and recommended to the user.

[0065] When it is detected that the user has purchased a certain product, the user's associated intended products are obtained, and associated intended products that are in the same second associated group or first associated group as the purchased product are recommended to the user.

[0066] As a preferred solution of the survey and promotion method based on shared information described in this application, product promotion to users based on the high-intent products and related-intent products also includes scene-linked promotion; the scene-linked promotion method is as follows:

[0067] Obtain the user's high-intent products and related-intent products, and form a product recommendation list;

[0068] Extracting the user's device information from the session shared information; determining the common geographic location of the user's device based on the device information; and calculating the purchase probability of each product in the product recommendation list in each common geographic location in combination with the product purchase history;

[0069] Mark the common geographical locations with the highest purchase probability as the associated geographical locations of the corresponding products;

[0070] If it is detected that the user's device is in the geographical location associated with any product in the product recommendation list, the corresponding product is recommended to the user.

[0071] In a second aspect, the present application provides a survey and promotion system based on shared information, including an information acquisition module, a behavior recognition module, a product survey module, and a product promotion module; wherein:

[0072] The information acquisition module is used to obtain the user's session sharing information;

[0073] The behavior recognition module recognizes the user's behavior pattern based on the session shared information;

[0074] The product research module obtains users' high-intent products based on their browsing behavior patterns, and obtains users' related-intent products based on their purchasing behavior patterns;

[0075] The product promotion module is used to promote products to users, including regular promotion and scenario-linked promotion.

[0076] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0077] By obtaining session shared information including user identification, product browsing history, product purchase history, device and environment information, we can comprehensively and deeply analyze user behavior, understand the user's attention to product pages and browsing order, discover non-intuitive correlations between products, and provide strong support for in-depth understanding of user consumption habits and preferences. By analyzing the user's browsing and purchasing behavior patterns, we can obtain the user's high-intention products and related-intention products respectively, so that product recommendations are more in line with user needs, improve the accuracy of product promotion and the probability of users purchasing related products. The product promotion methods adopted in this application cover conventional promotion and scene-linked promotion. It can not only recommend products in real time based on the user's browsing and purchasing behavior, but also actively recommend products to users in appropriate scenarios, thereby enhancing the product promotion effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] To more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0079] Figure 1A flowchart of the survey and promotion method based on shared information provided for this application;

[0080] Figure 2 A schematic diagram of the structure of the survey and promotion system based on shared information provided for this application. DETAILED DESCRIPTION

[0081] The technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0082] Example 1

[0083] This embodiment introduces a survey and promotion method based on shared information. Figure 1 , the method comprises the following steps:

[0084] Get the user's session sharing information;

[0085] The user's session shared information includes user identification, product browsing history, product purchase history, device and environment information;

[0086] User identifiers are used to identify users. Common user identifiers include user ID, login account, mobile phone number, and email address. User identifiers can link a user's browsing and purchasing behavior across different time periods and pages. Product browsing history includes browsing history for any product category, specifically including parameters such as the URL (page address) of the page the user viewed and the duration of the page. By analyzing page dwell time, it is possible to determine the user's level of interest in the content of different pages within a particular product category. Product purchase history includes parameters such as product information, quantity, and purchase time. Product purchase history directly reflects user consumption preferences and provides an important basis for identifying related products. Device and environment information includes the type of device used by the user (e.g., mobile phone, computer, tablet), device IP address, and device geographic location. This information helps understand the user's device usage and purchasing scenarios, enabling targeted product promotion based on different devices and locations.

[0087] Session shared information refers to data that the server shares across multiple requests to maintain user state. In many application scenarios, such as adding items to an e-commerce website's shopping cart, accessing product pages, and logging in and out, the server needs to be able to identify these requests as coming from the same session (i.e., user conversation). When a user first accesses the server, the server creates a new session and generates a unique session ID. The server then passes this session ID to the client, typically by setting a cookie. The client includes this session ID in each subsequent request, allowing the server to identify it as a request from the same session. The server then searches for the corresponding session data based on the received session ID to restore the user's state. Session data is typically stored on the server, while the user only saves the session ID, which helps protect sensitive information. Existing technologies include various methods for obtaining session shared information, such as through cookies, databases, and cache servers.

[0088] Identify the user's behavior pattern based on the session shared information; specifically, identify the user's browsing behavior pattern and purchasing behavior pattern;

[0089] Browsing behavior patterns represent the order in which users browse and the level of attention they pay to product-related pages when browsing a particular category of products. Users' browsing behavior is often sequential (for example, they look at price first, then reviews). Identifying this order and matching it with other users' browsing patterns can help predict which product within the same category a user might be most interested in, thereby providing product recommendations that better suit their needs. For example, on an e-commerce platform, when purchasing a laptop, a user first checks the price range of different brands, then browses user reviews of each product, and finally focuses on the computer's specifications. Analyzing this series of browsing behaviors using algorithms such as Markov chain models reveals that users with similar browsing sequences are highly likely to purchase a specific laptop after viewing the specifications. Therefore, recommendations for that laptop can be made to improve the accuracy of product promotions and the likelihood of users purchasing related products.

[0090] The method for identifying the user's browsing behavior pattern is as follows:

[0091] Each product browsing record of the user for products of the same type is extracted from the session shared information; wherein each product browsing record corresponds to a specific product of the same type of goods.

[0092] Count the types of related pages for the same type of product and assign a number to each type of related page; for example, the related pages of a certain type of product include price page, evaluation page, parameter configuration page, after-sales service page, etc.

[0093] Based on the product browsing records and the numbers of the related pages, a browsing feature sequence and a browsing order matrix are established to record the user's browsing behavior pattern.

[0094] The browsing feature sequence is used to record the user's attention to each related page; the browsing feature sequence is a vector of length N; where N is the number of related pages of the same type of product; each element in the browsing feature sequence corresponds to a related page;

[0095] The method of establishing a browsing feature sequence is as follows:

[0096] Sort N related pages and record the numbers of the related pages based on the sorting; this sorting is arbitrary because the browsing feature sequence is only used to record the user's attention to each related page, and is not used to record the user's browsing order between different pages.

[0097] Count the total browsing time of each related page in all product browsing records, and calculate the total browsing time of all related pages;

[0098] Calculate the element value corresponding to each number; combine all element values ​​into a browsing feature sequence; the element value corresponding to any number is the ratio of the total browsing time of the relevant page corresponding to the number to the total browsing time of all relevant pages.

[0099] The browsing order matrix is ​​used to record the order in which users browse different related pages. The method for establishing the browsing order matrix is ​​as follows:

[0100] Sort the numbers of N related pages horizontally and vertically to form a browsing order matrix with N rows and N columns, and initialize each element in the browsing order matrix;

[0101] Based on the product browsing history, the user's browsing path and the transition probability of each browsing path are counted; any browsing path includes two related pages, namely the first page and the second page;

[0102] make Indicates that the first page is the i-th related page and the second page is the j-th related page. The browsing path is The transition probability is denoted as , represents the probability that a user browses the jth related page after browsing the ith related page; the value ranges of i and j are 1, 2, ..., N;

[0103] Assign values ​​to the elements in the browsing order matrix based on the transition probability of each browsing path; where the element value in the i-th row and j-th column of the browsing order matrix is .

[0104] The identifying of the user's browsing behavior pattern also includes dynamically modifying the browsing order matrix, specifically including:

[0105] Identify the user's dependent browsing path; set a transition probability threshold, and mark the browsing path with a transition probability greater than the transition probability threshold as a dependent browsing path; for example, if the probability that the user browses the review page after browsing the price page is higher than 70%, then mark the browsing path in which the first page is the price page and the second page is the review page as a dependent browsing path.

[0106] If it is detected that the user is browsing the first page of the dependent browsing path, select a page from the related pages not included in the dependent browsing path as the third page; and push the third page to the user through a pop-up window;

[0107] Collect user feedback on pop-up window push notifications; the feedback includes explicit positive feedback, implicit positive feedback, and negative feedback. If the user jumps to and browses the third page through the pop-up window, the feedback is explicit positive feedback; if the user closes the pop-up window and manually jumps to and browses the third page after finishing browsing the first page, the feedback is implicit positive feedback; if the user closes the pop-up window and does not jump to the third page after finishing browsing the first page, the feedback is negative feedback.

[0108] The browsing order matrix is ​​modified based on the operational feedback, including:

[0109] Let the first page of the dependent browsing path be the pth related page, the second page of the dependent browsing path be the qth related page, and the third page be the rth related page; the value ranges of p, q, and r are all 1, 2, ..., N; if the user's operation feedback is explicit positive feedback, then the browsing path The transition probability Reduce , and browse to the path The transition probability Increase ; The first correction value is; if the user's operation feedback is implicit positive feedback, the browsing path The transition probability Reduce , and browse to the path The transition probability Increase ; is the preset second correction value; and The best selection can be verified based on a large number of experiments. If the user's operation feedback is negative, then keep and Preferably, if the user's operation feedback is negative feedback, the browsing path Adding to the ignore list means that when it is detected that the user is browsing the pth related page in the future, the rth related page will no longer be selected as the third page for pop-up push.

[0110] The method for dynamically correcting the browsing order matrix provided in the embodiments of the present application, based on traditional probability statistics, interactively verifies the stability of user behavior patterns through active pop-up window push notifications. Combined with user feedback on pop-up window pages, the browsing order matrix is ​​corrected to better reflect the user's true intentions. The pop-up window push notifications verify whether the user is blindly following historical paths. If the user's browsing behavior changes significantly due to pop-up window interference, it indicates that the original browsing order matrix fails to reflect the user's true needs. This solves the problem of historical statistical data being unable to capture the dynamic changes in user focus.

[0111] Purchasing behavior patterns indicate that when users purchase a particular product, they may also purchase other related products. Using algorithms like association rule learning, we can uncover the hidden purchasing behavior patterns within users' product purchase records. This can reveal non-intuitive product connections, helping us identify products that are not immediately obvious but are actually closely related, providing a basis for product research and promotion strategies. For example, after purchasing infant formula, users may also purchase baby bottles, and then perhaps baby food. By discovering this purchasing behavior pattern through algorithms, we can accurately recommend bottles and food to users who purchase formula, and also understand the potential connections between these products.

[0112] Here are some ways to identify users’ purchasing behavior patterns:

[0113] Extracting the user's product purchase records from the session shared information, and extracting each product purchased by the user from the product purchase records; assigning a product number to each product purchased by the user;

[0114] Record the purchase time and corresponding product number of each product purchased by the user, and establish the user's purchase time series; each position in the purchase time series corresponds to a time point (this time point is specific to a certain hour or minute of a certain year, month, and day); if the user did not purchase a product at any time point, the position corresponding to the time point is empty; if the user purchased a product at any time point, the element value of the position corresponding to the time point is the product number;

[0115] Setting the length and sliding step of the sliding window; intercepting the purchase time series based on the sliding window and the sliding step; and creating a purchase event based on each intercepted sequence segment;

[0116] A purchase event table is established based on the product number and purchase event to record the user's purchase behavior pattern.

[0117] Any column in the purchase event table corresponds to the product number of a product; any row in the purchase event table corresponds to a purchase event;

[0118] The method to create a purchase event table is as follows:

[0119] Create an empty purchase event table based on the number of product numbers and the number of purchase events;

[0120] The empty purchase event table is assigned values ​​based on the product number contained in each purchase event, as follows: in any purchase event, the cell at the corresponding position in the purchase event table for any product purchased by the user is assigned a value of 1, and the cell at the corresponding position in the purchase event table for any product not purchased by the user is assigned a value of 0.

[0121] Acquire the user's high-intent products based on the browsing behavior pattern; acquire the user's associated intention products based on the purchasing behavior pattern;

[0122] High-intent products refer to specific products with a relatively high purchase probability among the same category of products browsed by users.

[0123] The method for obtaining users’ high-intent products based on their browsing behavior patterns is as follows:

[0124] Obtain other users' product browsing and purchase records;

[0125] Based on product browsing and purchase records, filter out other users who have browsed and purchased the same type of products and mark them as reference users;

[0126] Establish a browsing feature sequence and browsing order matrix for each reference user;

[0127] For any reference user, calculate the similarity between the browsing feature sequence of the user and the reference user and the similarity between the browsing order matrix and sum them up to get the similarity between the user and the reference user;

[0128] Setting a similarity threshold; marking reference users whose similarity to the user is higher than the similarity threshold as similar users;

[0129] Based on the product purchase records, the specific models of the same type of products purchased by each similar user are extracted; the purchase frequency of each specific model of the product is counted, and the m products with the most purchase frequency are marked as the high-intent products.

[0130] Related intention products mean that after purchasing a product, users are more likely to purchase other related products.

[0131] The method for obtaining the user's associated intended products based on the purchase behavior pattern is as follows:

[0132] Set relevance threshold and purchase count threshold;

[0133] Combining any two products in the purchase event table whose purchase times are both greater than the purchase times threshold to obtain a first association group; any first association group contains two products with different product numbers;

[0134] Count the association degree of each first association group, and delete the first association groups with an association degree less than the association degree threshold; the association degree of the first association group is the number of times the first association group appears in the purchase event table divided by the total number of purchase events; for example, a first association group contains two products with product numbers A and B, and in the purchase event table, there are k rows (i.e., k purchase events) in which the values ​​of the cells corresponding to product numbers A and B are both 1, then k is the number of times the first association group appears in the purchase event table.

[0135] Merge any two first association groups and remove duplicates to obtain a second association group; calculate the association degree of each second association group and delete any second association groups with an association degree less than a predetermined association degree threshold; the association degree of the second association group is the number of times the second association group appears in the purchase event table divided by the total number of purchase events; and save the first and second association groups. Deduplication is performed because the merged first association groups may contain identical second association groups. For example, if three first association groups with product numbers AB, AC, and BC are merged, they will form three identical second association groups, each with product number ABC.

[0136] Extract a list of all products purchased by the user in the last n days based on the product purchase records; n is a positive integer; combine the first association group and the second association group to obtain the user's associated intended products.

[0137] The step of combining the first association group and the second association group to obtain the user's associated intended products specifically includes:

[0138] S101: If there is a second association group containing any two purchased products, and there is a product in the second association group that the user has not purchased, then mark the product in the second association group that the user has not purchased as an association intention product;

[0139] S102: If no associated intended product is obtained in S101, the associated intended product is obtained through the first associated group, as follows:

[0140] If the first association group contains any purchased product, and there is a product in the first association group that the user has not purchased, then the product in the first association group that the user has not purchased is marked as an association intention product;

[0141] All associated intended products are sorted based on the association degrees of the corresponding first associated groups, and the associated intended products corresponding to the M first associated groups with the largest association degrees are retained, where M is a positive integer.

[0142] Product promotion is performed to users based on the high-intent products and related-intent products, including conventional promotion and scenario-linked promotion; wherein the conventional promotion method includes:

[0143] Real-time detection of user browsing and purchasing behavior based on session shared information. When a user is detected to have finished browsing any type of product and no purchases of the same type are detected, the user's high-intent products are obtained and recommended to the user.

[0144] When it is detected that the user has purchased a certain product, the user's associated intended products are obtained, and associated intended products that are in the same second associated group or first associated group as the purchased product are recommended to the user.

[0145] The method of scene linkage promotion is as follows:

[0146] Obtain the user's high-intent products and related-intent products, and form a product recommendation list;

[0147] Extract the user's device information from the session shared information; determine the user's device's frequently used geographic location based on the device information; calculate the purchase probability of each product in the product recommendation list at each frequently used geographic location, based on the product purchase history; for any product A, its purchase probability at any frequently used geographic location D is calculated as follows: obtain the number of purchases of all products of the same type as product A at each frequently used geographic location in the user's product purchase history; the purchase probability of product A at frequently used geographic location D The calculation formula is as follows:

[0148] ;

[0149] in, Indicates the number of purchases of all products of the same type as product A in a common location D. Indicates the total number of purchases of all products of the same type as product A in all common geographic locations.

[0150] Mark the common geographical locations with the highest purchase probability as the associated geographical locations of the corresponding products;

[0151] If it is detected that the user's device is in the geographical location associated with any product in the product recommendation list, the corresponding product is recommended to the user.

[0152] Example 2

[0153] This embodiment is the second embodiment of the present application; it is based on the same inventive concept as embodiment 1, and Figure 2 This embodiment introduces a survey and promotion system based on shared information, including an information acquisition module, a behavior recognition module, a product survey module, and a product promotion module; wherein:

[0154] The information acquisition module is used to obtain user session shared information, including user ID, product browsing history, product purchase history, and device and environment information. This information is the basis for subsequent analysis and decision-making, used to identify users, analyze user behavior, and understand their device usage and purchase scenarios.

[0155] The behavior recognition module identifies user behavior patterns based on the session shared information. User behavior patterns include browsing and purchasing patterns. This module extracts user browsing records for the same product type from the session shared information, counts and numbers the relevant page types, and establishes a browsing feature sequence and browsing order matrix to record user browsing behavior patterns and help predict products of interest. This module is also used to obtain user product purchase records, extract and number the purchased products, establish a purchase time series, and create purchase events by setting a sliding window to intercept the sequence. This module then establishes a purchase event table to record purchase behavior patterns and explore potential relationships between products.

[0156] The product research module identifies users' high-intent products based on their browsing behavior patterns and their associated products based on their purchasing behavior patterns. This module identifies high-intent products by obtaining browsing and purchasing records from other users, screening reference users, calculating similarity with the target user, tagging similar users, and counting the models of products purchased by similar users. This module determines associated products by setting a correlation threshold and a purchase count threshold, combining products in the purchase event table, calculating correlations, merging and removing duplicates, and then combining the user's recently purchased product list to determine associated products.

[0157] The product promotion module is used to promote products to users, including both regular and scenario-based promotions. This module performs regular promotions by monitoring users' browsing and purchasing behavior in real time. If users browse products but don't purchase, it recommends high-intent products; after a user purchases a product, it recommends related products. This module performs scenario-based promotions by identifying the user's device's frequently used geographic locations, calculating the purchase probability of products in each frequently used location, marking the related locations, and recommending the corresponding products when the user's device is in the related location.

[0158] The specific functional implementation of each of the above modules refers to the relevant content of the survey and promotion method based on shared information described in Example 1 and will not be elaborated on here.

[0159] If the technical solution disclosed herein involves personal information, the product using the technical solution disclosed herein has clearly informed the individual of the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution disclosed herein involves sensitive personal information, the product using the technical solution disclosed herein has obtained the individual's separate consent before processing the sensitive personal information and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the individual has entered the personal information collection scope and that personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information. The personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0160] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] The above describes the embodiments of the present application in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose and scope of protection of this application, all of which are protected by this application.

Claims

1. A survey and promotion method based on shared information, characterized by: The following steps are involved: Get the user's session sharing information; Identify the user's behavior pattern based on the session shared information; specifically, identify the user's browsing behavior pattern and purchasing behavior pattern; The method for identifying the user's browsing behavior pattern is as follows: Extracting each product browsing record of the user for the same type of product from the session shared information; Count the types of related pages for the same type of product and assign a number to each type of related page; Based on the product browsing records and the numbers of the related pages, a browsing feature sequence and a browsing order matrix are established to record the user's browsing behavior pattern; The browsing feature sequence is used to record the user's attention to each related page; The browsing feature sequence is a vector of length N, where N is the number of related pages for the same type of product. Each element in the browsing feature sequence corresponds to a related page. The method of establishing a browsing feature sequence is as follows: Sort N related pages and record the numbers of the related pages based on the sorting; Count the total browsing time of each related page in all product browsing records, and calculate the total browsing time of all related pages; Calculate the element value corresponding to each number; combine all element values ​​into a browsing feature sequence; the element value corresponding to any number is the ratio of the total browsing time of the relevant page corresponding to the number to the total browsing time of all relevant pages; The browsing order matrix is ​​used to record the order in which users browse different related pages. The method for establishing the browsing order matrix is ​​as follows: Sort the numbers of N related pages horizontally and vertically to form a browsing order matrix with N rows and N columns, and initialize each element in the browsing order matrix; Based on the product browsing history, the user's browsing path and the transition probability of each browsing path are counted; any browsing path includes two related pages, namely the first page and the second page; make Indicates that the first page is the i-th related page and the second page is the j-th related page. The browsing path is The transition probability is denoted as , represents the probability that a user browses the jth related page after browsing the ith related page; Assign values ​​to the elements in the browsing order matrix based on the transition probability of each browsing path; where the element value in the i-th row and j-th column of the browsing order matrix is ; Here are some ways to identify users’ purchasing behavior patterns: Extracting the user's product purchase records from the session shared information, and extracting each product purchased by the user from the product purchase records; assigning a product number to each product purchased by the user; Record the purchase time and corresponding product number of each product purchased by the user, and establish the user's purchase time series; each position in the purchase time series corresponds to a time point; if the user does not purchase a product at any time point, the position corresponding to the time point is empty; if the user purchases a product at any time point, the element value of the position corresponding to the time point is the product number; Setting the length and sliding step of the sliding window; intercepting the purchase time series based on the sliding window and the sliding step; and creating a purchase event based on each intercepted sequence segment; Based on the product number and purchase event, a purchase event table is established to record the user's purchase behavior pattern; Acquire the user's high-intent products based on the browsing behavior pattern; acquire the user's associated intention products based on the purchasing behavior pattern; The method for obtaining users’ high-intent products based on their browsing behavior patterns is as follows: Obtain other users' product browsing and purchase records; Based on product browsing and purchase records, filter out other users who have browsed and purchased the same type of products and mark them as reference users; Establish a browsing feature sequence and browsing order matrix for each reference user; For any reference user, calculate the similarity between the browsing feature sequence of the user and the reference user and the similarity between the browsing order matrix and sum them up to get the similarity between the user and the reference user; Setting a similarity threshold; marking reference users whose similarity to the user is higher than the similarity threshold as similar users; Extract the specific models of the same type of products purchased by each similar user based on the product purchase records; count the number of purchases of each specific model of product, and mark the m products with the most purchases as the high-intent products; The method for obtaining the user's associated intended products based on the purchase behavior pattern is as follows: Set relevance threshold and purchase count threshold; Combining any two products in the purchase event table whose purchase times are both greater than the purchase times threshold to obtain a first association group; any first association group contains two products with different product numbers; Counting the relevance of each first association group, and deleting the first association group whose relevance is less than the relevance threshold; the relevance of the first association group is the number of times the first association group appears in the purchase event table divided by the total number of purchase events; Merge any two first association groups and remove duplicates to obtain a second association group; count the association degree of each second association group and delete the second association group whose association degree is less than the association degree threshold; the association degree of the second association group is the number of times the second association group appears in the purchase event table divided by the total number of purchase events; save the first association group and the second association group; Extract a list of all products purchased by the user in the last n days based on the product purchase records; n is a positive integer; combine the first association group and the second association group to obtain the user's associated intended products; Product promotion is performed to users based on the high-intent products and related-intent products.

2. The survey and promotion method based on shared information according to claim 1, characterized in that: The identifying of the user's browsing behavior pattern also includes dynamically modifying the browsing order matrix, specifically including: Identify the user's dependent browsing path; set a transition probability threshold, and mark the browsing path with a transition probability greater than the transition probability threshold as a dependent browsing path; If it is detected that the user is browsing the first page of the dependent browsing path, select a page from the related pages not included in the dependent browsing path as the third page; and push the third page to the user through a pop-up window; Collect user feedback on pop-up push notifications; the feedback includes explicit positive feedback, implicit positive feedback, and negative feedback; The browsing order matrix is ​​modified based on the operation feedback.

3. The survey and promotion method based on shared information according to claim 2, characterized in that: If the user jumps to and browses the third page through the pop-up window, the operation feedback is explicit positive feedback; if the user closes the pop-up window and manually jumps to and browses the third page after finishing browsing the first page, the operation feedback is implicit positive feedback; if the user closes the pop-up window and does not jump to the third page after finishing browsing the first page, the operation feedback is negative feedback; Modifying the browsing order matrix based on the operation feedback specifically includes: Let the first page that depends on the browsing path be the pth related page, the second page that depends on the browsing path be the qth related page, and the third page be the rth related page; if the user's operation feedback is explicit positive feedback, then the browsing path The transition probability Reduce Δ , and browse to the path The transition probability Increase Δ ; Δ is the preset first correction amount; if the user's operation feedback is implicit positive feedback, the browsing path The transition probability Reduce Δ , and browse to the path The transition probability Increase Δ ; Δ is the preset second correction value; if the user's operation feedback is negative feedback, then keep and constant.

4. The survey and promotion method based on shared information according to claim 3, characterized in that: Any column in the purchase event table corresponds to the product number of a product; any row in the purchase event table corresponds to a purchase event; The method to create a purchase event table is as follows: Create an empty purchase event table based on the number of product numbers and the number of purchase events; The empty purchase event table is assigned values ​​based on the product number contained in each purchase event, as follows: in any purchase event, the cell at the corresponding position in the purchase event table for any product purchased by the user is assigned a value of 1, and the cell at the corresponding position in the purchase event table for any product not purchased by the user is assigned a value of 0.

5. The survey and promotion method based on shared information according to claim 4, characterized in that: The step of combining the first association group and the second association group to obtain the user's associated intended products specifically includes: S101: If there is a second association group containing any two purchased products, and there is a product in the second association group that the user has not purchased, then mark the product in the second association group that the user has not purchased as an association intention product; S102: If no associated intended product is obtained in S101, the associated intended product is obtained through the first associated group, as follows: If the first association group contains any purchased product, and there is a product in the first association group that the user has not purchased, then the product in the first association group that the user has not purchased is marked as an association intention product; All associated intended products are sorted based on the association degrees of the corresponding first associated groups, and the associated intended products corresponding to the M first associated groups with the largest association degrees are retained.

6. The survey and promotion method based on shared information according to claim 5, characterized in that: Promoting products to users based on the high-intent products and related-intent products, including regular promotions; The conventional promotion methods include: Real-time detection of user browsing and purchasing behaviors based on session shared information; When it is detected that the user has finished browsing any type of products and no user has purchased the same type of products, the user's high-intention products are obtained and high-intention products of the same type as the browsed products are recommended to the user; When it is detected that the user has purchased a certain product, the user's associated intended products are obtained, and associated intended products that are in the same second associated group or first associated group as the purchased product are recommended to the user.

7. The survey and promotion method based on shared information according to claim 6, characterized in that: Product promotion to users based on the high-intent products and related-intent products also includes scene linkage promotion; the scene linkage promotion method is as follows: Obtain the user's high-intent products and related-intent products, and form a product recommendation list; Extracting the user's device information from the session shared information; and determining a common geographic location of the user's device based on the device information; Calculating the purchase probability of each product in the product recommendation list in each common geographical location based on the product purchase records; Mark the common geographical locations with the highest purchase probability as the associated geographical locations of the corresponding products; If it is detected that the user's device is in the geographical location associated with any product in the product recommendation list, the corresponding product is recommended to the user.

8. A survey and promotion system based on shared information, which is used to implement the survey and promotion method based on shared information according to any one of claims 1 to 7, characterized in that: It includes information acquisition module, behavior recognition module, product investigation module, and product promotion module; among them: The information acquisition module is used to obtain the user's session sharing information; The behavior recognition module recognizes the user's behavior pattern based on the session shared information; The product research module obtains users' high-intent products based on their browsing behavior patterns, and obtains users' related-intent products based on their purchasing behavior patterns; The product promotion module is used to promote products to users, including regular promotion and scenario-linked promotion.

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