Cross-border e-commerce user portrait analysis method and system

Abstract

The invention is suitable for the technical field of user portrait analysis, and particularly relates to a cross-border e-commerce user portrait analysis method and system, and the method comprises the steps: obtaining corresponding behavior information when detecting that a user clicks a plurality of commodities; when the collection operation is triggered, marking the corresponding commodity as a first commodity, and when the user continues to click a second commodity, performing analysis according to the behavior information to obtain a first attention feature chain and a second attention feature chain; analyzing the first attention feature chain and the second attention feature chain to obtain a commodity attention sequence; and performing analysis according to the commodity attention sequence to obtain commodity push information. According to the cross-border e-commerce user portrait analysis method and system provided by the invention, the commodities can be effectively recommended according to the specific attribute preference weights of the users for the commodities, so that the selection cost of the users is reduced, the conversion rate of the platform is improved, and the satisfaction degree of the users is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of user portrait analysis, and in particular, relates to a method and system for cross-border e-commerce user portrait analysis. Background Art User portrait analysis refers to a systematic method of collecting, integrating and analyzing various types of user data (such as behavior, preferences, needs, background, etc.) to construct a virtual typical user model (i.e. user portrait) in order to deeply understand the characteristics of the target user group and guide product design, marketing strategy formulation and other work.

[0002] In the related art, in the personalized recommendation system of the current e-commerce platform, the construction of user portraits mainly relies on the statistical analysis of historical behavior data and the matching algorithm of product labels. Usually, by collecting explicit behavior data such as user clicks, collections, and purchase records, combined with static attributes such as product categories, price ranges, and basic functional parameters, a product recommendation system is established to achieve product recommendations. Traditional recommendation systems cannot effectively recommend products based on the user's preference weights for specific attributes of products. For example, the user's demand for a pair of headphones is first a long battery life function and then a headset with a price of 200 yuan. The user has collected a headset with a high battery life of 300 yuan, but has not collected a headset with a price of 200 yuan but without a long battery life function. The traditional recommendation system will only recommend based on the collected 300 yuan headset with a long battery life function, ignoring the price demand of the user's demand order below the high battery life function, resulting in a deviation between the recommendation results of the product and the user's real needs, which ultimately leads to an increase in user selection costs, a decrease in platform conversion rate, and a decrease in user experience satisfaction. Summary of the invention

[0003] The embodiments of the present application provide a method and system for analyzing user portraits of cross-border e-commerce, which can improve the problem of being unable to effectively recommend products based on the user's preference weights for specific attributes of the products, resulting in increased user selection costs, decreased platform conversion rates, and reduced user experience satisfaction.

[0004] In a first aspect, the present application embodiment provides a method for analyzing a cross-border e-commerce user portrait, including: When it is detected that a user clicks on multiple products, corresponding behavior information is obtained; wherein the multiple products refer to products of the same category, and the behavior information includes browsing behavior information and interactive behavior information, wherein the browsing behavior information is used to reflect the browsing data of the user when browsing the product homepage; and the interactive behavior information is used to reflect the interactive data when the user interacts on the product homepage; When the collection operation is triggered, the corresponding product is marked as the first product. When the user continues to click on the second product, the behavior information is analyzed to obtain the first attention feature chain and the second attention feature chain. The collection operation is used to reflect that the user has collected or added the product to the shopping cart. The first attention feature chain is used to reflect the user's attention to different features of the first product. The second attention feature chain is used to reflect the user's attention to different features of the second product. The second product is used to reflect the product that has not been collected. Analyze the first attention feature chain and the second attention feature chain to obtain a product attention sequence; wherein the product attention sequence is used to reflect the order of user's demand for different features of a certain category of products, and the demand refers to the user's attention to the feature; The commodity push information is obtained by analyzing the commodity attention sequence; wherein the commodity push information is used to reflect the commodity recommended to the user.

[0005] The above technical solutions in the embodiments of the present application have at least the following technical effects: The cross-border e-commerce user portrait analysis method provided in the embodiment of the present application first detects that a user enters and clicks on multiple products of the same category, and obtains corresponding behavior information including browsing behavior information for reflecting the data generated when the user browses the first product and interaction behavior information for reflecting the data generated when the user interacts with the first product. When the collection operation is triggered, the corresponding product is marked as the first product. When the user continues to click on the second product that reflects the product that has not been collected, analysis is performed based on the behavior information to obtain a first attention feature chain for reflecting the user's different features of the first product and a second attention feature chain for reflecting the user's attention to the different features of the second product. Analysis is then performed based on the first attention feature chain and the second attention feature chain to obtain a product attention sequence of the user's preference for product features of the product category. Finally, analysis is performed based on the product attention sequence to obtain product push information reflecting the products recommended for the user. This method can effectively compare and analyze the degree of attention paid to different features of the first product in the first state with the degree of attention paid to different features of the second product in the second state, and obtain the priority order of users' preferences for the features of products in this category. The cross-border e-commerce user portrait analysis system can then recommend products in this category to users according to the priority order, which can reduce users' selection costs, increase the platform's conversion rate, and improve user satisfaction.

[0006] In a possible implementation manner of the first aspect, analyzing according to the behavior information to obtain the first focus feature chain and the second focus feature chain includes: confirming the behavior information of the first commodity as first behavior information; wherein the first behavior information is used to reflect the behavior information of the first commodity; Integrate the behavior information of the plurality of second commodities to obtain a second behavior information set; wherein the second behavior information set is used to reflect the collection of the behavior information of the plurality of second commodities; Analyze the first behavior information to obtain a first focus feature chain; Multi-feature classification is performed according to the second behavior information set to obtain a second focus feature chain.

[0007] In a possible implementation manner of the first aspect, the analyzing according to the first behavior information to obtain a first focus feature chain includes: Analyze the browsing behavior information of the first behavior information to obtain a browsing path, browsing time corresponding to each node, and information entropy; wherein the browsing path is used to reflect the browsing order of the user on the display page of the product, the display page includes a picture display page, a parameter display page, and a comment display page, and the browsing time is used to reflect the user's stay time on the display page of the product; the information entropy is used to reflect the information richness of the display page of the product, and the node is used to reflect the different browsing contents of the browsing path; Analyze the interactive behavior information of the first behavior information and the browsing path to obtain a sliding speed corresponding to each node; wherein the sliding speed is used to reflect the speed at which the user slides the product display page; Analyze the browsing time, the sliding speed and the information entropy corresponding to each node to obtain a dimension attention priority sequence; wherein the dimension attention priority sequence is used to reflect the user's priority attention to different display pages of the product; The browsing path is processed according to the dimensional focus priority sequence to obtain a first focus feature chain.

[0008] In a possible implementation manner of the first aspect, performing multi-feature classification according to the second behavior information set to obtain a second focus feature chain includes: Collecting features of the plurality of commodities corresponding to the second behavior information set to obtain a feature set; wherein the feature set is used to reflect the feature set of the plurality of commodities in the second behavior information set; Performing repeated feature division processing on the feature set to obtain a plurality of repeated features and a plurality of non-repeated features; wherein the repeated features are used to reflect the same features among the plurality of commodities, and the non-repeated features are used to reflect the different features among the plurality of commodities; Performing an occurrence frequency analysis on the plurality of non-repeating features to obtain a plurality of required features; wherein the required features are used to reflect features whose occurrence frequency of the non-repeating features is greater than or equal to a preset occurrence frequency; Confirming the plurality of repeated features and the plurality of required features as a plurality of necessary features; wherein the necessary features are used to reflect the features that the user must possess when browsing the same type of the products; The plurality of necessary features are arranged in the priority sequence of the dimensional focus to obtain a second focus feature chain.

[0009] In a possible implementation manner of the first aspect, analyzing according to the browsing time, the sliding speed, and the information entropy corresponding to each node to obtain a dimension attention priority sequence includes: Obtaining a reference sliding speed; wherein the reference sliding speed is used to reflect the speed at which a user slides a product display page during normal browsing; Processing is performed according to the reference sliding speed and the sliding speed of each node to obtain a first weight of each node; wherein the first weight is used to reflect the degree of influence of the sliding speed on the attention of the node; Processing is performed according to the browsing time corresponding to each node to obtain a second weight corresponding to each node; wherein the second weight is used to reflect the influence of the browsing time on the attention of the node; An analysis is performed based on the first weight, the second weight and the information entropy to obtain a dimensional attention priority sequence.

[0010] In a possible implementation manner of the first aspect, the analyzing according to the first weight, the second weight, and the information entropy to obtain a dimension attention priority sequence includes: Dimensional classification is performed according to the information entropy corresponding to each node to obtain first-dimensional information entropy and second-dimensional information entropy; wherein the first-dimensional information entropy is used to reflect the information complexity of the display page in terms of image vision, and the second-dimensional information entropy is used to reflect the information complexity of the display page in terms of text language; Processing is performed according to the first dimension information entropy, the second weight corresponding to the corresponding node, and the first weight to obtain the first dimension attention ratio; wherein the first dimension attention ratio is used to reflect the user's attention to the first dimension; the first dimension is used to reflect the picture display page of the display page; The second dimension information entropy, browsing time and first weight corresponding to each node are processed and compared to obtain a second dimension attention sequence; wherein the second dimension attention sequence is used to reflect the user's attention order to the second dimension, and the second dimension is used to reflect the parameter display page and the comment display page of the display page; Confirm the first dimension attention ratio and the second dimension attention sequence as the dimension attention priority sequence.

[0011] In a possible implementation of the first aspect, the obtaining of the second dimension attention sequence by performing processing and comparison according to the second dimension information entropy, browsing time, and first weight corresponding to each node includes: The attention ratio is obtained by comparing the browsing time corresponding to each node with the information entropy of the second dimension; wherein the attention ratio is used to reflect the user's attention allocation status at each node in the second dimension; Processing the attention proportion corresponding to each node and the first weight to obtain the attention degree; wherein the attention degree is used to reflect the user's attention to each node in the second dimension; The nodes of the second dimension are sorted in descending order of the attention degree to obtain a second dimension attention sequence.

[0012] In a possible implementation manner of the first aspect, analyzing the first attention feature chain and the second attention feature chain to obtain a product attention sequence includes: Comparing the first dimension attention ratio in the first attention feature chain with the first dimension attention ratio in the second attention feature chain, obtaining a first dimension change status; wherein the first dimension change status is used to reflect the user's attention change to the first dimension; Comparing the corresponding dimensional features of the second dimensional attention sequence in the first attention feature chain with the second dimensional attention sequence in the second attention feature chain, obtaining a second dimensional difference feature; wherein the second dimensional difference feature is used to reflect the difference feature of the corresponding node in the second dimensional attention sequence of the user; According to the comparison between the first dimension change status and the preset change status, a first dimension attention feature is obtained; wherein the first dimension attention feature is used to reflect the user's attention feature in the first dimension, and the preset change status is used to reflect a preset change status value; The first dimension attention feature and the second dimension difference feature are confirmed as a product attention sequence.

[0013] In a possible implementation manner of the first aspect, obtaining the first-dimensional attention feature by comparing the first-dimensional change status with a preset change status includes: When the first dimension change condition is greater than or equal to the preset change condition, confirming the first dimension feature in the first focus feature chain as the first dimension focus feature; When the first dimensional change condition is smaller than the preset change condition, the first dimensional feature in the second focus feature chain is confirmed as the first dimensional focus feature.

[0014] In a second aspect, the embodiment of the present application provides a cross-border e-commerce user portrait analysis system, including: An acquisition module, configured to acquire corresponding behavior information when detecting that a user clicks on multiple products; wherein the multiple products refer to products of the same category, and the behavior information includes browsing behavior information and interactive behavior information, wherein the browsing behavior information is used to reflect the browsing data of the user when browsing the product homepage; and the interactive behavior information is used to reflect the interactive data when the user interacts on the product homepage; A first analysis module is used to mark the corresponding product as the first product when the collection operation is triggered, and when the user continues to click on the second product, analyze according to the behavior information to obtain a first attention feature chain and a second attention feature chain; wherein the collection operation is used to reflect that the user has collected or added the product to the shopping cart, the first attention feature chain is used to reflect the user's attention to different features of the first product, the second attention feature chain is used to reflect the user's attention to different features of the second product, and the second product is used to reflect the product that has not been collected; A second analysis module is used to analyze the first attention feature chain and the second attention feature chain to obtain a product attention sequence; wherein the product attention sequence is used to reflect the order of user's demand for different features of a certain category of products, and the demand refers to the user's attention to the feature; The push module is used to analyze the commodity attention sequence to obtain commodity push information; wherein the commodity push information is used to reflect the commodity recommended to the user.

[0015] In a third aspect, an embodiment of the present application provides a cross-border e-commerce user portrait analysis device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements a method as described in any one of the first aspects above.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the first aspects above is implemented.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program. When the computer program runs on a cross-border e-commerce user portrait analysis device, the cross-border e-commerce user portrait analysis device executes the cross-border e-commerce user portrait analysis method described in any one of the first aspects above.

[0018] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 It is a flowchart of a cross-border e-commerce user portrait analysis method provided by an embodiment of the present application; Figure 2 This is a schematic diagram of the implementation process of the cross-border e-commerce user portrait analysis method provided by an embodiment of the present application; Figure 3 It is a structural diagram of a cross-border e-commerce user portrait analysis system provided by an embodiment of the present application; Figure 4 It is a structural diagram of a cross-border e-commerce user portrait analysis device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0022] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0023] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0024] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0025] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0027] In the related art, in the personalized recommendation system of the current e-commerce platform, the construction of user portraits mainly relies on the statistical analysis of historical behavior data and the matching algorithm of product labels. Usually, by collecting explicit behavior data such as user clicks, collections, and purchase records, combined with static attributes such as product categories, price ranges, and basic functional parameters, a product recommendation system is established to achieve product recommendations. Traditional recommendation systems cannot effectively recommend products based on the user's preference weights for specific attributes of products. For example, the user's order of demand for a pair of headphones is first a long battery life function and then a headphone priced at 200 yuan. The user has collected a high-battery life headphone priced at 300 yuan, but has not collected a headphone priced at 200 yuan but without a high-battery life function. The traditional recommendation system will only recommend based on the collected 300 yuan headphones with a high-battery life function, ignoring the price demand of the user's demand order below the high-battery life function, resulting in a deviation between the recommendation results of the product and the user's real needs, which ultimately leads to an increase in user selection costs, a decrease in platform conversion rate, and a decrease in user experience satisfaction.

[0028] To solve the above problems, the embodiment of the present application provides a method and system for analyzing user portraits of cross-border e-commerce. In the method, when a user enters and clicks on multiple commodities of the same category, the corresponding behavior information including browsing behavior information for reflecting the data generated when the user browses the first commodity and interactive behavior information for reflecting the data generated when the user interacts with the first commodity is obtained. When the collection operation is triggered, the corresponding commodity is marked as the first commodity. When the user continues to click on the second commodity for reflecting the commodity that has not been collected, the behavior information is analyzed again to obtain a first attention feature chain for reflecting the user's different features of the first commodity and a second attention feature chain for reflecting the user's attention to the different features of the second commodity. Then, the first attention feature chain and the second attention feature chain are analyzed to obtain a commodity attention sequence of the user's preference for commodity features of commodity categories. Finally, the commodity attention sequence is analyzed to obtain commodity push information for reflecting the commodity recommended for the user. This method can effectively compare and analyze the degree of attention paid to different features of the first product in the first state with the degree of attention paid to different features of the second product in the second state, and obtain the priority order of users' preferences for the features of products in this category. The cross-border e-commerce user portrait analysis system can then recommend products in this category to users according to the priority order, which can reduce users' selection costs, increase the platform's conversion rate, and improve user satisfaction.

[0029] The cross-border e-commerce user portrait analysis method provided in the embodiment of the present application can be applied to a cross-border e-commerce user portrait analysis device. At this time, the cross-border e-commerce user portrait analysis device is the executor of the cross-border e-commerce user portrait analysis method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the cross-border e-commerce user portrait analysis device.

[0030] For example, the cross-border e-commerce user portrait analysis device can be a station (STAION, ST) in a WLAN, which can be a mobile phone, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, desktop computer, smart large screen, computing device or other processing device connected to a wireless modem, computer, laptop computer, handheld computing device, for example, a mobile terminal in a 5G network or a mobile terminal in a future evolved public land mobile network (Public Land Mobile Network, PLMN), etc.

[0031] In order to better understand the cross-border e-commerce user portrait analysis method provided in the embodiment of the present application, the specific implementation process of the cross-border e-commerce user portrait analysis method provided in the embodiment of the present application is exemplarily introduced below.

[0032] Figure 1 and Figure 2 A schematic flow chart of the cross-border e-commerce user portrait analysis method provided in the embodiment of the present application is shown. Figure 1 and Figure 2 , the cross-border e-commerce user portrait analysis methods include: S100, when it is detected that a user clicks on multiple products, corresponding behavior information is obtained; wherein, multiple products refer to products of the same category, and the behavior information includes browsing behavior information and interactive behavior information. The browsing behavior information is used to reflect the browsing data of the user when browsing the product homepage; the interactive behavior information is used to reflect the interactive data when the user interacts on the product homepage.

[0033] It can be understood that after a user enters a product list or page of the same or similar category by searching keywords, he clicks on one of the products to enter the display page of the product, and the browsing of the product is completed until the user exits the browsing of the product, which is the behavior information corresponding to the product. Browsing data may include data such as browsing time and browsing content generated when the user browses the product. Interaction data may include data such as sliding speed and collection operations generated when the user browses the product page.

[0034] For example, browsing behavior information can be collected through front-end embedding, Cookies tracking, server logs and SDK to collect data such as page visits, dwell time and path to form the required browsing behavior information. Interaction behavior information relies on front-end embedding (including full embedding), API / crawler capture, database storage and back-end business data integration to record users' clicks, inputs, interactions and other operations on the product display page to form the required interaction behavior information.

[0035] S200, when the collection operation is triggered, the corresponding product is marked as the first product. When the user continues to click on the second product, the behavior information is analyzed to obtain the first attention feature chain and the second attention feature chain. The collection operation is used to reflect the user's collection or addition to the shopping cart of the product. The first attention feature chain is used to reflect the user's attention to the different features of the first product. The second attention feature chain is used to reflect the user's attention to the different features of the second product. The second product is used to reflect the product that has not been collected.

[0036] It is understandable that when a user browses multiple products of the same category in a product list, if he does not collect a certain product, it may mean that none of the products the user browsed meets his intention. When a user collects a product during the browsing process, if he continues to click on other products and starts browsing, it means that the product collected by the user basically meets the user's purchase intention, but still does not fully meet the user's purchase needs.

[0037] Exemplarily, the interactive behavior information in the behavior information can be analyzed. When it is detected that a user is browsing a product, the collection operation of the interactive behavior information in the behavior information corresponding to the product is triggered, and the product is determined as the first product. Then, when the user continues to click on the second product, the behavior information corresponding to multiple second products is integrated into a set of behavior information reflecting multiple second products. The behavior information is then analyzed based on the behavior information reflecting the first product to obtain the first attention feature chain. At the same time, the behavior information set used to reflect the behavior information of multiple second products is processed to obtain the second attention feature chain.

[0038] It is also possible to analyze the interactive behavior information, and when the customer service consultation in the interactive behavior information is triggered, obtain the consultation content information, perform language text analysis based on the consultation content, obtain consultation words, and then match the consultation words with the product features to obtain a matching result. When the matching result reflects a successful match, the product is determined to be the first product, and the behavior information of the product is determined to be the first behavior information. Otherwise, it is the second product and the corresponding second behavior information. Then, the behavior information corresponding to multiple second products is integrated into a set of behavior information reflecting multiple second products, and then analyzed based on the behavior information reflecting the first product to obtain a first attention feature chain. At the same time, it is processed according to the behavior information set reflecting the behavior information of multiple second products to obtain a second attention feature chain.

[0039] In a possible implementation, in step S200, when the collection operation is triggered and the user has not stopped browsing, analysis is performed based on the behavior information to obtain a first attention feature chain and a second attention feature chain, including: S210, confirming the behavior information of the first commodity as first behavior information; wherein the first behavior information is used to reflect the behavior information of the first commodity.

[0040] It can be understood that the behavior information corresponding to multiple products is divided according to whether the triggering operation in the behavior information corresponding to the product is triggered. That is, when the user browses the first product and collects the first product, the behavior information corresponding to the first product information is determined as the first behavior information.

[0041] S220, integrating the behavior information of the plurality of second commodities to obtain a second behavior information set; wherein the second behavior information set is used to reflect the collection of the behavior information of the plurality of second commodities.

[0042] It is understandable that because a user may generate multiple second commodities during browsing, the corresponding behavior information sets of the multiple second commodities may be collected as a second behavior information set.

[0043] S230: Analyze the first behavior information to obtain a first focus feature chain.

[0044] Exemplarily, by analyzing the browsing behavior information of the first behavior information, the user's browsing order of the product's picture display page, parameter display page, and comment display page, the user's corresponding dwell time on the product's picture display page, parameter display page, and comment display page, and the corresponding information richness of the product's picture display page, parameter display page, and comment display page can be obtained. Then, based on the interactive behavior information in the first behavior information and the user's browsing order of the product's picture display page, parameter display page, and comment display page, analysis can be performed to obtain the speed at which the user slides each display page corresponding to each node in the browsing order. Then, based on the user's dwell time on each display page corresponding to each node, the user's sliding speed of each display page, and the information richness of each display page, analysis can be performed to obtain the user's priority attention to different display pages of the product. Finally, the user's priority attention to different display pages of the product and the user's browsing order of each display page are processed to obtain the first attention feature chain.

[0045] It is also possible to analyze the interactive behavior information in the first behavior information to obtain consulting information between the user and the customer service, and then process the characteristic words and product characteristics in the consulting information to obtain multiple identical characteristics, and then sort the multiple identical characteristics in order of appearance to obtain the first focus feature chain.

[0046] In a possible implementation, in step S230, analyzing the first behavior information to obtain a first focus feature chain includes: S231, analyzing the browsing behavior information of the first behavior information to obtain the browsing path, the browsing time corresponding to each node and the information entropy; wherein the browsing path is used to reflect the user's browsing order of the product display page, the display page includes the picture display page, the parameter display page and the comment display page, and the browsing time is used to reflect the user's stay time on the product display page; the information entropy is used to reflect the information richness of the product display page, and the node is used to reflect the different browsing contents of the browsing path.

[0047] It can be understood that nodes refer to the different sections of the product display page that users browse when browsing a product. For example, when browsing headphones, a user first clicks on the picture section of the headphone product to observe, then slides to the comment section to browse, and finally browses the parameter display section of the headphone product. The nodes represent the content of the different sections browsed by the user.

[0048] Exemplarily, browsing data marked as the first behavior information is extracted from the behavior database, and data cleaning is performed to eliminate noise and outliers. Sequence analysis technology or visualization tools (such as D3.js's SunburstPartition graph) are used to mine the cleaned data to identify the user's mainstream browsing path, such as the typical trajectory from the product details page to the favorites. The dwell time of each path node is then calculated through timestamp association, and the information entropy of the display page corresponding to different nodes of the browsing path is calculated through a formula.

[0049] S232, analyzing the interactive behavior information and the browsing path of the first behavior information to obtain a sliding speed corresponding to each node; wherein the sliding speed is used to reflect the speed at which the user slides the product display page.

[0050] Exemplarily, the user's interactive behavior data (such as clicks, sliding operations) and browsing path trajectory are extracted from the first behavior information. For each path node, the user's touch events are monitored, the starting position coordinates, the ending position coordinates and the corresponding timestamp of the sliding operation are recorded, and then the displacement distance (horizontal or vertical coordinate difference) and time difference are calculated. Finally, the sliding speed is calculated node by node through the ratio formula of displacement and time difference (speed = displacement / time), so as to obtain the sliding speed of the user at different nodes.

[0051] S233, analyzing the browsing time, sliding speed and information entropy corresponding to each node to obtain a dimensional attention priority sequence; wherein the dimensional attention priority sequence is used to reflect the user's priority attention to different display pages of the product.

[0052] Exemplarily, the speed at which the user slides the product display page during normal browsing can be obtained, and then processed according to the reference sliding speed and the sliding speed of each node to obtain the user's initial interest in the content of the display page corresponding to each node of the browsing path. Then, it can be processed according to the browsing time corresponding to each node to obtain the user's progressive interest in the content of the display page corresponding to each node of the browsing path. Finally, the user's progressive interest in the content of the display page corresponding to each node of the browsing path, the user's initial interest in the content of the display page corresponding to each node of the browsing path, and the information entropy are analyzed to obtain the dimensional attention priority sequence.

[0053] The browsing time, sliding speed and information entropy corresponding to each node can also be analyzed by the first analysis model to obtain the dimension attention priority sequence, that is, the browsing time, sliding speed and information entropy corresponding to each node are input into the first analysis model, and the first analysis model then outputs the corresponding dimension attention priority sequence. The training process of the first analysis model can be performed by processing the browsing time, sliding speed and information entropy corresponding to each node with the dimension attention priority sequence as the training data set of the first analysis model, and then inputting the training data set of the first analysis model into the first analysis model for training and learning, and finally obtaining the first analysis model.

[0054] In a possible implementation, in step S233, analysis is performed based on the browsing time, sliding speed, and information entropy corresponding to each node to obtain a dimensional attention priority sequence, including: S2331, obtaining a reference sliding speed; wherein the reference sliding speed is used to reflect the speed at which a user slides a product display page during normal browsing.

[0055] It can be understood that the reference sliding speed is a preset sliding speed of the user.

[0056] For example, the reference sliding speed can be manually input, or directly obtained through a sliding database. The sliding database refers to a database containing the sliding speed of users when browsing commodities. These data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining, the collected data are sorted, classified and archived, useful information and rules are extracted, and relevant data are saved in the database to form a sliding database.

[0057] S2332, processing is performed according to the reference sliding speed and the sliding speed of each node to obtain a first weight of each node; wherein the first weight is used to reflect the influence of the sliding speed on the attention of the node.

[0058] It can be understood that the sliding speed of each node refers to the sliding speed of the user at each node of the browsing path, that is, the sliding speed of the user on different display pages of the product. When the user is interested in one display page in the product display page, the sliding speed will be less than or equal to the reference sliding speed, and when the user is interested in another display page in the product display page, the sliding speed will be greater than the reference sliding speed, and the user's interest in the product display page is negatively correlated with the sliding speed, that is, the faster the user slides in a product display page, the less interested the user is in the product display page. Each node is a display page of the product.

[0059] Exemplarily, the first weight can be expressed by calculating the relative speed difference between the reference sliding speed and the sliding speed of each node, that is, the first weight = (reference sliding speed - sliding speed) ÷ reference sliding speed. If there are three nodes and the sliding speed corresponding to the first node is 400px / s, the sliding speed corresponding to the second node is 500ps / s, and the sliding speed corresponding to the third node is 700px / s, and the reference sliding speed is 600px / s (px here is the height pixel unit of the page), then the first weight corresponding to the first node is 0.33 [(600-400) ÷ 600], the first weight corresponding to the second node is 0.17 [(600-500) ÷ 600], the first weight corresponding to the third node is -0.17 [(600-700) ÷ 600], and so on. It can be understood that the sign of the first weight refers to whether the user is interested in the product display page. When the first weight is a negative number, it reflects that the user is not interested in the product display page. When the first weight is a positive number, it reflects that the user is interested in the product display page. The absolute value of the first weight reflects the user's initial interest in the product display page.

[0060] S2333, performing processing according to the browsing time corresponding to each node to obtain a second weight corresponding to each node; wherein the second weight is used to reflect the influence degree of the browsing time on the attention of the node.

[0061] It can be understood that the second weight is the value of the browsing time corresponding to each node in the total browsing time, and the total browsing time refers to the duration from when the user starts to browse a product to when the user finishes browsing the product.

[0062] For example, if the user browses three product display pages, and the browsing time of the first display page is 30 seconds, the browsing time of the second display page is 40 seconds, and the browsing time of the third display page is 30 seconds, then the second weight corresponding to the first display page, i.e., the first node, is 0.3 [30÷(30+40+30)], the second weight corresponding to the second display page, i.e., the second node, is 0.4 [40÷(30+40+30)], the second weight corresponding to the third display page, i.e., the third node, is 0.3 [30÷(30+40+30)], and so on.

[0063] S2334, analyze according to the first weight, the second weight and the information entropy to obtain the dimension attention priority sequence.

[0064] Exemplarily, the information entropy corresponding to each node can be processed to obtain the information complexity of the display page in terms of image vision and the information complexity of the display page in terms of text language, and then the information complexity of the display page in terms of image vision, the corresponding second weight and the corresponding first weight are processed to obtain the user's attention to the image vision in the display page. At the same time, the information complexity of the display page in terms of text language, the corresponding browsing time and the first weight are processed to obtain the user's attention order between the parameter display page and the comment display page of the display page.

[0065] The first weight, the second weight and the information entropy can also be analyzed by a second analysis model to obtain a dimension attention priority sequence, that is, the first weight, the second weight and the information entropy are input into the second analysis model, and the second analysis model outputs the corresponding dimension attention priority sequence. The training process of the second analysis model can be performed by using the data processed by the first weight, the second weight and the information entropy and the dimension attention priority sequence as the training data set of the second analysis model, and then inputting the training data set of the second analysis model into the second analysis model for training and learning, and finally obtaining the second analysis model.

[0066] With this setting, the user's behavior pattern when browsing the product display page is analyzed through multi-dimensional data, the user's interest points are captured more accurately, the user's reference sliding speed is analyzed, and the first weight and the second weight are calculated by comparing the actual sliding speed with the reference sliding speed, combined with the user's stay time at each node. Then, combined with the concept of information entropy, these two weights are comprehensively analyzed to obtain the dimensional attention priority sequence. The dimensional attention priority sequence can help e-commerce platforms better understand user needs, and can also be used to optimize product recommendation algorithms and improve user experience, thereby improving conversion rates and user satisfaction.

[0067] In a possible implementation, in step S2334, an analysis is performed based on the first weight, the second weight, and the information entropy to obtain a dimensional attention priority sequence, including: S23341, perform dimension classification according to the information entropy corresponding to each node to obtain the first dimension information entropy and the second dimension information entropy; wherein the first dimension information entropy is used to reflect the information complexity of the display page in terms of image vision, and the second dimension information entropy is used to reflect the information complexity of the display page in terms of text language.

[0068] It can be understood that dimensional classification refers to classifying the information entropy corresponding to each node according to the image visual dimension and the text language dimension.

[0069] For example, the classification can be performed by the attributes corresponding to each node, or by the information entropy type corresponding to each node. The attributes corresponding to each node are the display content of the product display page. For example, the image display of the product on the product display page belongs to the image visual dimension, while the parameter introduction and comment feedback of the product on the product display page belong to the text language dimension, and so on.

[0070] S23342, processing is performed according to the first dimension information entropy, the second weight corresponding to the corresponding node, and the first weight to obtain the first dimension attention ratio; wherein, the first dimension attention ratio is used to reflect the user's attention to the first dimension; the first dimension is used to reflect the picture display page of the display page.

[0071] It can be understood that the first dimension concerns the proportion through the formula: Calculated, where The first dimension is the proportion of attention. is the first dimension information entropy, is the second weight, is the first weight.

[0072] For example, if , , , then the proportion of attention to the first dimension is 6.63 (6+0.3+×0.33), and so on.

[0073] S23343, according to the second dimension information entropy, browsing time and first weight corresponding to each node, the second dimension attention sequence is obtained after processing and comparison; wherein the second dimension attention sequence is used to reflect the user's attention order to the second dimension, and the second dimension is used to reflect the parameter display page and the comment display page of the display page.

[0074] Exemplarily, the browsing time of each node and the second dimension information entropy can be processed to obtain the user's attention distribution status to each node in the second dimension, and then the user's attention distribution status to each node in the second dimension and the first weight can be processed to obtain the user's attention level to each node in the second dimension, and then the second dimension attention sequence can be obtained by sorting each node in the second dimension according to the user's attention level.

[0075] It is also possible to obtain the information complexity ratio by normalizing the information entropy of each node in the second dimension, and then compare the browsing time corresponding to each node in the second dimension to obtain the browsing time ratio, and then analyze the browsing time ratio and the information complexity ratio to obtain the user's attention distribution status to each node in the second dimension, and then process the user's attention distribution status to each node in the second dimension and the first weight to obtain the user's attention to each node in the second dimension, and then sort the nodes in the second dimension according to the user's attention level to obtain the second dimension attention sequence.

[0076] In a possible implementation, in step S23343, the second dimension attention sequence is obtained by processing and comparing the second dimension information entropy, browsing time and first weight corresponding to each node, including: S233431, compare the browsing time corresponding to each node with the information entropy of the second dimension to obtain the attention ratio; wherein the attention ratio is used to reflect the user's attention allocation status on each node in the second dimension.

[0077] It can be understood that attention ratio = browsing time ÷ second dimension information entropy. Browsing time reflects the time users spend processing the corresponding second dimension information entropy. When the second dimension information entropy is high, users need to read and analyze more carefully, so browsing time is usually longer, which can reflect the user's attention allocation.

[0078] For example, if the browsing time is 30 seconds and the second dimension information entropy is 6 bits / character, then the attention share is 5 (30÷5), and so on.

[0079] S233432, processing is performed according to the attention proportion corresponding to each node and the first weight to obtain the attention degree; wherein the attention degree is used to reflect the user's attention degree to each node in the second dimension.

[0080] It can be understood that attention degree = attention ratio × first weight.

[0081] For example, if the attention ratio is 5 and the first weight is 0.33, the attention degree is 1.65, and so on.

[0082] S233433, sort the nodes of the second dimension in descending order of attention degree to obtain the second dimension attention sequence.

[0083] For example, if there are three nodes in the second dimension, and the attention degree corresponding to the first node is 1.65, the attention degree corresponding to the second node is 0.54, and the attention degree corresponding to the third node is 1.24, then the original order of first node → second node → third node is sorted in descending order of attention degree, and it becomes first node → third node → second node, and so on.

[0084] With this setting, the second dimension attention sequence is obtained through analysis, which can reveal the user's attention preference during the browsing process, and provide an important basis for e-commerce platforms to optimize the order of product display, improve user interaction and enhance personalized recommendation effects, thereby effectively improving user experience and platform operation efficiency.

[0085] S23344, confirm the first dimension attention ratio and the second dimension attention sequence as the dimension attention priority sequence.

[0086] It can be understood that by analyzing different dimensions, we can get the first dimension attention ratio and the second dimension attention sequence, and thus get the dimension attention priority sequence. Because when users browse products, the image visual dimension occupies most of the product information, and only when they click on the product to enter the product details will the specific parameters and comments of the product be displayed.

[0087] With this setting, by dividing information entropy according to different dimensions, it is possible to analyze the degree of user attention to various product features of a category of goods from the two dimensions of visual display and text description, and conduct more refined classification and priority sorting, thereby achieving accurate positioning of user focus.

[0088] S234, processing the browsing path according to the dimension focus priority sequence to obtain the first focus feature chain.

[0089] Exemplarily, if the browsing path is A→B→C, where the dimensional attention sequence reflects that C's attention is greater than B, and A's attention is the smallest, then the first attention feature chain is C→B→A, reflecting the user's deep attention to specific content.

[0090] With this setting, through comprehensive analysis of multi-dimensional data (browsing path, dwell time, sliding speed, information entropy), the priority of users' attention features for a category of goods is constructed, and display formats that meet their attention priorities can be recommended to different user groups (such as user push technology details page of preference parameters), thereby effectively recommending products based on users' preference weights for specific attributes of products.

[0091] S240, performing multi-feature classification according to the second behavior information set to obtain a second focus feature chain.

[0092] Exemplarily, multiple commodities corresponding to the second behavior information set can be processed to obtain a feature set of multiple commodities in the second information set, and then processing can be performed based on the feature set of multiple commodities in the second information set to obtain the same features in the multiple commodities and the different features in the multiple commodities, and then processing can be performed based on the different features in the multiple commodities to obtain features whose occurrence frequency is greater than or equal to a preset occurrence frequency, and then the same features in the multiple commodities and the different features in the multiple commodities are determined to be features that users must have when browsing commodities of the same type, and finally the features that users must have when browsing commodities of the same type are sorted in an attention priority sequence to obtain a second attention feature chain.

[0093] It is also possible to perform feature analysis on the second behavior information to obtain multiple features in the second behavior information, then perform occurrence counts based on the multiple features to obtain a statistical histogram, and then determine the features in the statistical histogram that appear more than a preset number of times as features that users must have when browsing the same type of goods, and then match the features in the statistical histogram that appear less than a preset number of times with a preset feature database to obtain matching features, and then confirm the matching features as features that users must have when browsing the same type of goods, and finally sort the multiple required features in a priority sequence to obtain a second focus feature chain.

[0094] With this setting, by determining whether the collection operation is triggered, the behavioral information generated by the user browsing the product can be divided into the first behavioral information and the second behavioral information set. This can better refine the user behavior and conduct further analysis on it, improve the accuracy of personalized recommendations, optimize the user experience, and thus enhance the ability to recommend products that interest the user.

[0095] In a possible implementation, in step S240, multi-feature classification is performed according to the second behavior information set to obtain a second focus feature chain, including: S241, collecting features according to the multiple commodities corresponding to the second behavior information set to obtain a feature set; wherein the feature set is used to reflect the feature set of the multiple commodities in the second behavior information set.

[0096] Exemplarily, the features of multiple commodities in the second information set can be traversed by programming languages ​​(such as Python) and data processing libraries (such as Pandas), and the corresponding data can be extracted according to the defined feature list to generate a feature set. The feature list refers to the features of the commodity. The commodity features can be manually input. The commodity features can also be directly obtained through the feature database. The feature database refers to a database containing the features of different types of commodities. These data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining, the collected data will be sorted, classified and archived, useful information and rules will be extracted, and the relevant data will be saved in the database to form a feature database.

[0097] S242, performing repeated feature division processing on the feature set to obtain multiple repeated features and multiple non-repeated features; wherein the repeated features are used to reflect the same features in multiple commodities, and the non-repeated features are used to reflect the different features in multiple commodities.

[0098] For example, the feature set of each product can be analyzed by performing an intersection operation to find out the features common to all products, that is, repeated features, and the feature set of each product can be analyzed by performing a difference operation to find out the unique features of each product, that is, non-repeated features.

[0099] S243, performing an occurrence frequency analysis on the plurality of non-repeating features to obtain a plurality of required features; wherein the required features are used to reflect features whose occurrence frequency of the non-repeating features is greater than or equal to a preset occurrence frequency.

[0100] It can be understood that frequency analysis refers to the number of times a feature of a product appears in the number of browsed products. The preset occurrence frequency refers to the frequency of occurrence of a feature of a preset product.

[0101] Exemplarily, the preset frequency of occurrence can be manually input. The preset frequency of occurrence can also be directly obtained through a frequency database. A frequency database refers to a database containing the number of occurrences of characteristics of different types of goods. These data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining, the collected data is sorted, classified and archived, useful information and rules are extracted, and the relevant data is saved in the database to form a frequency database.

[0102] S244, confirming the multiple repeated features and the multiple required features as multiple necessary features; wherein the necessary features are used to reflect the features that the user must have when browsing the same type of products.

[0103] It is understandable that when browsing products, users may encounter multiple products with repeated product features. These repeated features may be basic attributes, functional descriptions or other common attributes of the products. In addition, each product may also have some unique required features, which reflect the personality or special attributes of the product. By integrating and confirming these repeated features with the required features, a set of multiple necessary features is formed.

[0104] S245, arranging the plurality of necessary features in a dimensional focus priority sequence to obtain a second focus feature chain.

[0105] It is understandable that the necessary features may be features reflected in different product display pages. For example, the features of the parameter display page in the product display page are different from the features of the review display page.

[0106] Exemplarily, if the dimension focus sequence shows that the priority of the parameter display page is higher than the priority of the comment display page, the features belonging to the parameter display page are arranged between the features of the comment display page.

[0107] With this setting, through feature collection, division, screening and sorting, it is possible to effectively identify and utilize the key features that users focus on when browsing products, thereby providing strong technical support for product recommendations, display optimization, product development and user satisfaction improvement on e-commerce platforms.

[0108] S300, analyzing the first attention feature chain and the second attention feature chain to obtain a product attention sequence; wherein the product attention sequence is used to reflect the order of user's demand for different features of a certain category of products, and demand refers to the user's attention level to the feature.

[0109] Exemplarily, the user's attention changes on the first dimension can be obtained by comparing the first dimension attention ratio in the first attention feature chain with the first dimension attention ratio in the second attention feature chain. At the same time, the second dimension attention sequence in the first attention feature chain and the second dimension attention sequence in the second attention feature chain are processed to obtain the difference characteristics of the corresponding nodes in the second dimension attention sequence of the user. Then, the user's attention changes on the first dimension and the pre-set change conditions are processed to obtain the user's attention characteristics in the first dimension. Finally, the difference characteristics between the user's attention characteristics in the first dimension and the corresponding nodes in the second dimension attention sequence of the user are confirmed as the product attention sequence.

[0110] The attention ratio can also be obtained by comparing the first dimension attention ratio in the first attention feature chain with the first dimension attention ratio in the second attention feature chain, and at the same time, the second dimension attention sequence in the first attention feature chain and the second dimension attention sequence in the second attention feature chain are processed to obtain the difference characteristics of the corresponding nodes in the second dimension attention sequence of the user, and then the attention ratio is compared with the value 1 to obtain the user's attention characteristics in the first dimension, and finally the difference characteristics of the user's attention characteristics in the first dimension and the corresponding nodes in the user's attention sequence in the second dimension are confirmed as the product attention sequence. Among them, the attention ratio refers to the first dimension attention ratio in the first attention feature chain divided by the first dimension attention ratio in the second attention feature chain.

[0111] In a possible implementation, in step S300, the first attention feature chain and the second attention feature chain are analyzed to obtain a commodity attention sequence, including: S310, comparing the first dimension attention ratio in the first attention feature chain with the first dimension attention ratio in the second attention feature chain to obtain the first dimension change status; wherein the first dimension change status is used to reflect the user's attention change to the first dimension.

[0112] It can be understood that the change status of the first dimension refers to the difference between the first dimension attention ratio in the first attention feature chain and the first dimension attention ratio in the second attention feature chain.

[0113] For example, if the first dimension attention ratio in the first attention feature chain is 2.022, and the second dimension attention ratio in the second attention feature chain is 3.05, then the first dimension change status is 1.028 (3.05-2.022), and so on.

[0114] S320, compare the corresponding dimensional features of the second dimensional attention sequence in the first attention feature chain with the second dimensional attention sequence in the second attention feature chain to obtain the second dimensional difference features; wherein the second dimensional difference features are used to reflect the difference features of the corresponding nodes in the second dimensional attention sequence of the user.

[0115] It can be understood that dimensional feature comparison refers to comparing features at the same node position, that is, comparing parameter display pages with parameter display pages, and comparing comment display pages with comment display pages. Difference features refer to the differences between features in the second dimensional focus sequence in the first focus feature chain and features in the second dimensional focus sequence in the second focus feature chain.

[0116] Exemplarily, if the features in the second dimension focus sequence in the first focus feature chain are high user experience-long battery life-high price, and the features in the second dimension focus sequence in the second focus feature chain are high user experience-low battery life-low price, then the second dimension difference features are high user experience-high battery life-low price, and so on.

[0117] S330, obtaining a first dimension attention feature according to the comparison between the first dimension change status and the preset change status; wherein the first dimension attention feature is used to reflect the user's attention feature in the first dimension, and the preset change status is used to reflect a preset change status value.

[0118] Exemplarily, the preset change status can be manually input, or the preset change status can be directly obtained through the change status database. The change status database refers to a database containing the characteristics of the change status of different types of commodities in the first dimension. These data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After obtaining, the collected data is sorted, classified and archived, useful information and rules are extracted, and the relevant data is saved in the database to form a change status database. The first dimension change status can be compared with the preset change status to obtain the first dimension focus features under different comparison results.

[0119] The comparison result can also be obtained by comparing the attention ratio with the value 1, and then the first dimension attention feature can be determined according to different comparison results. The comparison result refers to the sign after the comparison, that is, the comparison result is a positive number or a negative number. When the comparison result is a negative number, the first dimension attention feature is the first dimension feature in the second attention feature chain, and when the comparison result is a positive number, the first dimension attention feature is the first dimension feature in the first attention feature chain.

[0120] In a possible implementation, in step S330, the first dimension focus feature is obtained by comparing the first dimension change status with the preset change status, including: S331, when the first dimension change status is greater than or equal to the preset change status, the first dimension feature in the first focus feature chain is confirmed as the first dimension focus feature.

[0121] It can be understood that when the change status of the first dimension is greater than or equal to the preset change status, it means that the user's interest level in the visual dimension of the picture has not changed significantly, that is, compared with the products that have not been collected after browsing, the user prefers the picture display of the products that have been collected after browsing. At this time, the first dimension feature of the first attention feature chain is confirmed as the feature that the user is more interested in in terms of picture vision, that is, the first dimension attention feature.

[0122] S332, when the first dimension change condition is less than the preset change condition, the first dimension feature in the second focus feature chain is confirmed as the first dimension focus feature.

[0123] It can be understood that when the change in the first dimension is less than the preset change, it means that the user's interest in the visual dimension of the picture has changed significantly, that is, compared with the products that are collected after browsing, the user prefers the picture display of the products that are not collected after browsing. At this time, the first dimension feature of the second attention feature chain is confirmed as the feature that the user is more interested in in terms of picture vision, that is, the first dimension attention feature.

[0124] With such a setting, by comparing the changes in the first dimension, it is possible to analyze the changes in users' preferences for the appearance characteristics of a product category in the visual dimension of the picture, so that products with such appearance characteristics can be recommended to users from the picture, thereby increasing the user's click rate and further increasing the user's conversion rate.

[0125] S340, confirming the first dimension attention feature and the second dimension difference feature as a product attention sequence.

[0126] It can be understood that by determining the user's attention features in the first dimension and the user's difference features in the second dimension, it is possible to determine the user's attention sequence for a type of product.

[0127] For example, when a user is browsing products in the headphone category, the first dimension focus feature is headphones, and the second dimension difference feature reflects the features that users prefer, which are arranged in the order of high user experience - high battery life - low price. This shows that users prefer this category of products over headphones.

[0128] With this setting, by analyzing the user's attention characteristics in different dimensions, we can accurately capture the user's attention changes and difference characteristics, and construct a product attention sequence that reflects the user's real preferences, thereby providing strong supporting data for the e-commerce platform's personalized service product recommendations.

[0129] S400, analyzing the product attention sequence to obtain product push information; wherein the product push information is used to reflect the products recommended to the user.

[0130] It can be understood that by analyzing the product attention sequence, it is possible to clarify the user's interest in different features of a category of products in a category, and then according to the product attention sequence, the user's favorite products can be pushed before the user purchases the product, thereby allowing the user to purchase the product.

[0131] With such a setting, a comparative analysis is performed based on the degree of attention paid to different features of the first product in the first state and the degree of attention paid to different features of the second product in the second state, so as to obtain the priority order of users' preferences for the features of products in this category. Then, the cross-border e-commerce user portrait analysis system is used to recommend products in this category to users according to the priority order, which can reduce users' selection costs, improve the platform's conversion rate and enhance user satisfaction.

[0132] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0133] Corresponding to the cross-border e-commerce user portrait analysis method described in the above embodiment, the embodiment of the present application also provides a cross-border e-commerce user portrait analysis system, and each module of the cross-border e-commerce user portrait analysis system can implement each step of the cross-border e-commerce user portrait analysis method. Figure 3 A structural block diagram of the cross-border e-commerce user portrait analysis system provided in an embodiment of the present application is shown. For the sake of ease of explanation, only the parts related to the embodiment of the present application are shown.

[0134] Reference Figure 3 , the cross-border e-commerce user portrait analysis system includes: The acquisition module is used to acquire corresponding behavior information when detecting that a user clicks on multiple products; wherein, multiple products refer to products of the same category, and the behavior information includes browsing behavior information and interactive behavior information. The browsing behavior information is used to reflect the browsing data of the user when browsing the product homepage; the interactive behavior information is used to reflect the interactive data when the user interacts on the product homepage.

[0135] The first analysis module is used to mark the corresponding product as the first product when the collection operation is triggered. When the user continues to click on the second product, analysis is performed based on the behavior information to obtain the first attention feature chain and the second attention feature chain. Among them, the collection operation is used to reflect the user's collection or addition of the product to the shopping cart, the first attention feature chain is used to reflect the user's attention level to different features of the first product, the second attention feature chain is used to reflect the user's attention level to different features of the second product, and the second product is used to reflect the product that has not been collected.

[0136] The second analysis module is used to analyze the first focus feature chain and the second focus feature chain to obtain a product focus sequence; wherein the product focus sequence is used to reflect the order of user's demand for different features of a certain category of products, and demand refers to the user's attention to the feature.

[0137] The push module is used to analyze the product attention sequence and obtain product push information; wherein the product push information is used to reflect the products recommended to the user.

[0138] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0139] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0140] The present application also provides a cross-border e-commerce user portrait analysis device. Figure 4 This is a schematic diagram of the structure of a cross-border e-commerce user portrait analysis device 4 provided in an embodiment of the present application. Figure 4 As shown, the cross-border e-commerce user portrait analysis device 4 of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown), at least one memory 41 ( Figure 4 Only one is shown in the figure) and a computer program 42 stored in the at least one memory 41 and executable on the at least one processor 40. When the processor 40 executes the computer program 42, the cross-border e-commerce user portrait analysis device 4 implements the steps in any of the above-mentioned cross-border e-commerce user portrait analysis method embodiments, or implements the functions of each module / unit in the above-mentioned system embodiments.

[0141] Exemplarily, the computer program 42 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 42 in the cross-border e-commerce user portrait analysis device 4.

[0142] The cross-border e-commerce user portrait analysis device 4 can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The cross-border e-commerce user portrait analysis device 4 can include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art can understand that Figure 4 It is only an example of the cross-border e-commerce user portrait analysis device 4 and does not constitute a limitation of the cross-border e-commerce user portrait analysis device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, buses, etc.

[0143] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0144] In some embodiments, the memory 41 may be an internal storage unit of the cross-border e-commerce user portrait analysis device 4, such as a hard disk or memory of the cross-border e-commerce user portrait analysis device 4. In other embodiments, the memory 41 may also be an external storage device of the cross-border e-commerce user portrait analysis device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the cross-border e-commerce user portrait analysis device 4. Further, the memory 41 may also include both the internal storage unit and the external storage device of the cross-border e-commerce user portrait cross-border e-commerce user portrait analysis device 4. The memory 41 is used to store operating systems, applications, boot loaders (BootLoader), data and other programs, such as program codes of computer programs, etc. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0145] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0146] An embodiment of the present application provides a computer program product. When the computer program product runs on a cross-border e-commerce user portrait analysis device, the cross-border e-commerce user portrait analysis device implements the steps in any of the above-mentioned method embodiments.

[0147] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. According to this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the cross-border e-commerce user portrait analysis device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0148] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0149] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0150] In the embodiments provided in the present application, it should be understood that the disclosed cross-border e-commerce user portrait analysis system and device can be implemented in other ways. For example, the cross-border e-commerce user portrait analysis system embodiment described above is only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0151] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for analyzing cross-border e-commerce user portraits, characterized in that: include: When it is detected that a user clicks on multiple products, corresponding behavior information is obtained; wherein the multiple products refer to products of the same category, and the behavior information includes browsing behavior information and interactive behavior information, wherein the browsing behavior information is used to reflect the browsing data of the user when browsing the product homepage; and the interactive behavior information is used to reflect the interactive data when the user interacts on the product homepage; When the collection operation is triggered, the corresponding product is marked as the first product. When the user continues to click on the second product, the behavior information is analyzed to obtain the first attention feature chain and the second attention feature chain. The collection operation is used to reflect that the user has collected or added the product to the shopping cart. The first attention feature chain is used to reflect the user's attention to different features of the first product. The second attention feature chain is used to reflect the user's attention to different features of the second product. The second product is used to reflect the product that has not been collected. Analyze the first attention feature chain and the second attention feature chain to obtain a product attention sequence; wherein the product attention sequence is used to reflect the order of user's demand for different features of a certain category of products, and the demand refers to the user's attention to the feature; The commodity push information is obtained by analyzing the commodity attention sequence; wherein the commodity push information is used to reflect the commodity recommended to the user.

2. The cross-border e-commerce user portrait analysis method according to claim 1, characterized in that: The analyzing according to the behavior information to obtain the first focus feature chain and the second focus feature chain includes: confirming the behavior information of the first commodity as first behavior information; wherein the first behavior information is used to reflect the behavior information of the first commodity; Integrate the behavior information of the plurality of second commodities to obtain a second behavior information set; wherein the second behavior information set is used to reflect the collection of the behavior information of the plurality of second commodities; Analyze the first behavior information to obtain a first focus feature chain; Multi-feature classification is performed according to the second behavior information set to obtain a second focus feature chain.

3. The cross-border e-commerce user portrait analysis method according to claim 2, characterized in that: The step of analyzing the first behavior information to obtain a first focus feature chain includes: Analyze the browsing behavior information of the first behavior information to obtain a browsing path, browsing time corresponding to each node, and information entropy; wherein the browsing path is used to reflect the browsing order of the user on the display page of the product, the display page includes a picture display page, a parameter display page, and a comment display page, and the browsing time is used to reflect the user's stay time on the display page of the product; the information entropy is used to reflect the information richness of the display page of the product, and the node is used to reflect the different browsing contents of the browsing path; Analyze the interactive behavior information of the first behavior information and the browsing path to obtain a sliding speed corresponding to each node; wherein the sliding speed is used to reflect the speed at which the user slides the product display page; Analyze the browsing time, the sliding speed and the information entropy corresponding to each node to obtain a dimension attention priority sequence; wherein the dimension attention priority sequence is used to reflect the user's priority attention to different display pages of the product; The browsing path is processed according to the dimensional focus priority sequence to obtain a first focus feature chain.

4. The cross-border e-commerce user portrait analysis method according to claim 3, characterized in that: The performing multi-feature classification according to the second behavior information set to obtain a second focus feature chain includes: Collecting features of the plurality of commodities corresponding to the second behavior information set to obtain a feature set; wherein the feature set is used to reflect the feature set of the plurality of commodities in the second behavior information set; Performing repeated feature division processing on the feature set to obtain a plurality of repeated features and a plurality of non-repeated features; wherein the repeated features are used to reflect the same features among the plurality of commodities, and the non-repeated features are used to reflect the different features among the plurality of commodities; Performing an occurrence frequency analysis on the plurality of non-repeating features to obtain a plurality of required features; wherein the required features are used to reflect features whose occurrence frequency of the non-repeating features is greater than or equal to a preset occurrence frequency; Confirming the plurality of repeated features and the plurality of required features as a plurality of necessary features; wherein the necessary features are used to reflect the features that the user must possess when browsing the same type of the products; The plurality of necessary features are arranged in the priority sequence of the dimensional focus to obtain a second focus feature chain.

5. The cross-border e-commerce user portrait analysis method according to claim 3, characterized in that: The analyzing according to the browsing time, the sliding speed and the information entropy corresponding to each node to obtain the dimension attention priority sequence includes: Obtaining a reference sliding speed; wherein the reference sliding speed is used to reflect the speed at which a user slides a product display page during normal browsing; Processing is performed according to the reference sliding speed and the sliding speed of each node to obtain a first weight of each node; wherein the first weight is used to reflect the degree of influence of the sliding speed on the attention of the node; Processing is performed according to the browsing time corresponding to each node to obtain a second weight corresponding to each node; wherein the second weight is used to reflect the influence of the browsing time on the attention of the node; An analysis is performed based on the first weight, the second weight and the information entropy to obtain a dimensional attention priority sequence.

6. The cross-border e-commerce user portrait analysis method according to claim 5, characterized in that: The analyzing according to the first weight, the second weight and the information entropy to obtain the dimension attention priority sequence includes: Dimensional classification is performed according to the information entropy corresponding to each node to obtain first-dimensional information entropy and second-dimensional information entropy; wherein the first-dimensional information entropy is used to reflect the information complexity of the display page in terms of image vision, and the second-dimensional information entropy is used to reflect the information complexity of the display page in terms of text language; Processing is performed according to the first dimension information entropy, the second weight corresponding to the corresponding node, and the first weight to obtain the first dimension attention ratio; wherein the first dimension attention ratio is used to reflect the user's attention to the first dimension; the first dimension is used to reflect the picture display page of the display page; The second dimension information entropy, browsing time and first weight corresponding to each node are processed and compared to obtain a second dimension attention sequence; wherein the second dimension attention sequence is used to reflect the user's attention order to the second dimension, and the second dimension is used to reflect the parameter display page and the comment display page of the display page; Confirm the first dimension attention ratio and the second dimension attention sequence as the dimension attention priority sequence.

7. The cross-border e-commerce user portrait analysis method according to claim 6, characterized in that: The second dimension information entropy, browsing time and first weight corresponding to each node are processed and compared to obtain the second dimension attention sequence, including: The attention ratio is obtained by comparing the browsing time corresponding to each node with the information entropy of the second dimension; wherein the attention ratio is used to reflect the user's attention allocation status at each node in the second dimension; Processing the attention proportion corresponding to each node and the first weight to obtain the attention degree; wherein the attention degree is used to reflect the user's attention to each node in the second dimension; The nodes of the second dimension are sorted in descending order of the attention degree to obtain a second dimension attention sequence.

8. The cross-border e-commerce user portrait analysis method according to claim 1, characterized in that: The analyzing the first attention feature chain and the second attention feature chain to obtain a commodity attention sequence includes: Comparing the first dimension attention ratio in the first attention feature chain with the first dimension attention ratio in the second attention feature chain, obtaining a first dimension change status; wherein the first dimension change status is used to reflect the user's attention change to the first dimension; Comparing the corresponding dimensional features of the second dimensional attention sequence in the first attention feature chain with the second dimensional attention sequence in the second attention feature chain, obtaining a second dimensional difference feature; wherein the second dimensional difference feature is used to reflect the difference feature of the corresponding node in the second dimensional attention sequence of the user; According to the comparison between the first dimension change status and the preset change status, a first dimension attention feature is obtained; wherein the first dimension attention feature is used to reflect the user's attention feature in the first dimension, and the preset change status is used to reflect a preset change status value; The first dimension attention feature and the second dimension difference feature are confirmed as a product attention sequence.

9. The cross-border e-commerce user portrait analysis method according to claim 8, characterized in that: The step of comparing the first dimension change status with a preset change status to obtain the first dimension focus feature includes: When the first dimension change condition is greater than or equal to the preset change condition, confirming the first dimension feature in the first focus feature chain as the first dimension focus feature; When the first dimensional change condition is smaller than the preset change condition, the first dimensional feature in the second focus feature chain is confirmed as the first dimensional focus feature.

10. A cross-border e-commerce user portrait analysis system, characterized in that: include: An acquisition module, configured to acquire corresponding behavior information when detecting that a user clicks on multiple products; wherein the multiple products refer to products of the same category, and the behavior information includes browsing behavior information and interactive behavior information, wherein the browsing behavior information is used to reflect the browsing data of the user when browsing the product homepage; and the interactive behavior information is used to reflect the interactive data when the user interacts on the product homepage; A first analysis module is used to mark the corresponding product as the first product when the collection operation is triggered, and when the user continues to click on the second product, analyze according to the behavior information to obtain a first attention feature chain and a second attention feature chain; wherein the collection operation is used to reflect that the user has collected or added the product to the shopping cart, the first attention feature chain is used to reflect the user's attention to different features of the first product, the second attention feature chain is used to reflect the user's attention to different features of the second product, and the second product is used to reflect the product that has not been collected; A second analysis module is used to analyze the first attention feature chain and the second attention feature chain to obtain a product attention sequence; wherein the product attention sequence is used to reflect the order of user's demand for different features of a certain category of products, and the demand refers to the user's attention to the feature; The push module is used to analyze the commodity attention sequence to obtain commodity push information; wherein the commodity push information is used to reflect the commodity recommended to the user.

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