A data analysis method for e-commerce platforms

Abstract

The present invention relates to the field of data analysis, and in particular to a data analysis method for an e-commerce platform. The present invention creates a user behavior database and a product information database; calculates a feature highlighting representation value for the user behavior feature based on the user behavior feature of the user to determine whether the user behavior feature is a reference behavior feature; determines a correlation consistency coefficient based on the correlation between the product keyword corresponding to the reference behavior feature and the historical product keyword corresponding to the user's historical user behavior feature to determine the direction category of the user behavior feature; and adaptively pushes products to the user based on the direction category of the user behavior feature. The present invention can improve the accuracy of product push to the user on the platform side while ensuring data reliability.

Description

Technical Field

[0001] The present invention relates to the field of data analysis, and in particular to an e-commerce platform data analysis method. Background Art

[0002] Consumers' shopping habits are gradually shifting from offline to online, and their demand for personalized products and services is growing. E-commerce platforms can use the massive user behavior data they have accumulated and analyze big data to help them better understand and meet consumer needs, further optimize products and services, provide consumers with a better online shopping experience, and improve operational efficiency and market competitiveness.

[0003] Chinese patent publication number: CN116342230A, discloses an e-commerce data storage platform based on big data analysis, including a data acquisition and cleaning module, a data storage and management module, and a data analysis and modeling module; the data acquisition and cleaning module includes a data acquisition unit and a data cleaning unit; the data acquisition unit is used to collect product-related data; the data cleaning unit includes a data value processing program, a data formatting program, a data integration program, a data deduplication program, and a data sorting program; the data storage and management module includes a database selection and database design unit, a database table creation unit, a data import unit, and a data partitioning unit; the data analysis and modeling module analyzes user purchasing behavior and product attributes based on the data processed by the data acquisition and cleaning module, and establishes a product recommendation model to improve user purchase conversion rate and sales.

[0004] However, the prior art still has the following problems:

[0005] Although the method of pushing relevant product information based on the user's behavioral characteristics is considered, it does not take into account that when the user's behavior is unclear, the pushed product information may be inaccurate and unreliable. Summary of the Invention

[0006] To this end, the present invention provides an e-commerce platform data analysis method to overcome the problem in the prior art that, although relevant product information is pushed based on the user's behavioral characteristics, the problem of unclear user behavior directionality is not considered, resulting in inaccurate and unreliable pushed product information.

[0007] To achieve the above objectives, the present invention provides an e-commerce platform data analysis method, which includes:

[0008] Create a user behavior database and a product information database. The user behavior database is used to obtain and store user behavior characteristics in real time, and the product information database is used to store the association relationship between each product keyword.

[0009] The user behavior characteristics include a product page refresh frequency and a product page pull completeness for a single product page, and the association relationship is determined based on a plurality of user behavior characteristics stored in the user behavior database;

[0010] Calculating a feature highlighting representation value for the user behavior feature based on the user's user behavior feature to determine whether the user behavior feature is a reference behavior feature, and determining a correlation consistency coefficient based on the correlation between the product keyword corresponding to the reference behavior feature and the historical product keyword corresponding to the user's historical user behavior feature to determine the category that the user behavior feature points to;

[0011] Pushing products to users based on the categories of user behavior characteristics, including:

[0012] Based on the product page corresponding to the user behavior feature, a reference keyword is locked, several derivative keywords associated with the reference keyword are determined, several product pages containing the derivative keywords are determined, and a push frequency for each of the product pages is determined based on the feature highlighting representation value, so as to push the product page to the user;

[0013] Alternatively, the user's historical user behavior characteristics are randomly screened to determine the corresponding product page, extract product keywords, and push the product page containing the product keywords to the user.

[0014] Furthermore, the process of determining the association relationship of product keywords includes:

[0015] Calling the user behavior characteristics of several users to determine the product keywords in the user's single browsing of several product pages;

[0016] Calculate the co-occurrence probability of a single product keyword and the remaining product keywords in a user's single browsing;

[0017] If the co-occurrence probability between a single product keyword and any product keyword is greater than a predetermined co-occurrence probability threshold, it is determined that there is an association relationship between the single product keyword and any product keyword;

[0018] Among them, a single browsing is from the time when the user starts browsing the product page through the platform to the time when the user closes the platform.

[0019] Furthermore, the process of calculating the feature highlighting representation value for the user behavior feature based on the user behavior feature includes:

[0020] Calculate the ratio of the product page refresh frequency to the preset refresh frequency threshold as the first feature highlighting coefficient;

[0021] The completeness of the product page is used as the second feature highlighting coefficient;

[0022] A feature salient characterization value is obtained by weightedly summing the first feature salient characterization coefficient and the second feature salient characterization coefficient.

[0023] Further, determining whether the user behavior feature is a reference behavior feature includes:

[0024] If the feature salience representation value is greater than or equal to the feature salience representation value threshold, the user behavior feature is determined to be a reference behavior feature.

[0025] Furthermore, the process of determining the correlation consistency coefficient includes,

[0026] Determine the product page corresponding to the user's reference behavior characteristics, and extract product keywords from the product page;

[0027] Determining historical user behavior characteristics of the user within a predetermined period in the user behavior database, and extracting historical product keywords from product pages;

[0028] Determining the average correlation between each of the product keywords and the historical product keywords;

[0029] The association degree mean is determined as the association consistency coefficient.

[0030] Furthermore, determining the category of the user behavior feature includes:

[0031] If the correlation consistency coefficient is greater than or equal to the correlation consistency coefficient threshold, the user behavior feature is determined to be a clear pointing category;

[0032] If the correlation consistency coefficient is less than the correlation consistency coefficient threshold, the user behavior feature is determined to be a fuzzy pointing category.

[0033] Furthermore, pushing products to users based on the categories indicated by the user behavior characteristics includes:

[0034] If the user behavior feature clearly points to a category, then based on the product page corresponding to the user behavior feature, a reference keyword is locked, several derivative keywords associated with the reference keyword are determined, several product pages containing the derivative keywords are determined, and a push frequency for each of the product pages is determined based on the feature prominence representation value, so as to push the product page to the user;

[0035] If the user behavior feature is a fuzzy pointing category, the user's historical user behavior features are randomly screened, the corresponding product page is determined to extract product keywords, and the product page containing the product keywords is pushed to the user.

[0036] Furthermore, the process of locking reference keywords on the product page corresponding to the user behavior characteristics includes:

[0037] Determining the product page corresponding to the user behavior characteristics;

[0038] A number of product keywords are extracted from the product page, and the frequency of occurrence of each product keyword is calculated, and the product keyword corresponding to the highest frequency is locked as a reference keyword.

[0039] Furthermore, the process of determining a number of derived keywords associated with the reference keyword includes:

[0040] Retrieving from the product information database several other product keywords that are associated with the reference keyword;

[0041] The called commodity keywords are determined as derived keywords.

[0042] Furthermore, the push frequency for each product page is determined based on the feature highlighting representation value, including:

[0043] The push frequency for each of the product pages is increased, and the increase in push frequency is positively correlated with the feature highlighting representation value.

[0044] Compared with the prior art, the present invention creates a user behavior database and a product information database; calculates a feature highlighting representation value for the user behavior feature based on the user behavior feature to determine whether the user behavior feature is a reference behavior feature, determines a correlation consistency coefficient based on the correlation between the product keyword corresponding to the reference behavior feature and the historical product keyword corresponding to the user's historical user behavior feature to determine the direction category of the user behavior feature; and adaptively pushes products to users based on the direction category of the user behavior feature. The present invention can improve the accuracy of product push to users on the platform side while ensuring data reliability.

[0045] In particular, the present invention takes into account the association relationship between several keywords in each product page covered by the platform, and determines other products that the user may have potential purchasing tendencies through the association relationship. In actual situations, the user's user behavior can reflect whether the user has potential associated purchasing tendencies for the product. By identifying the keywords in each product page to build an association relationship, after subsequently locking the product that the user currently has potential associated purchasing tendencies, support is provided for identifying the products corresponding to other product keywords that have an association relationship with the product keywords of the above-mentioned product, so as to facilitate accurate product push to users.

[0046] In particular, the present invention calculates the feature-highlighting representation value for the user behavior feature based on the user's user behavior feature. In actual situations, due to the unclear directionality of a single behavior, it is impossible to clearly determine the user's purchasing intention, which may lead to inaccurate content pushed by the platform. The present application calculates the feature-highlighting representation value for the user behavior feature to represent the degree of potential purchasing intention reflected by the user behavior, and determines the user behavior feature with clarity as the reference behavior feature. Furthermore, in order to avoid the low accuracy of the data represented by the reference behavior feature alone, the correlation consistency coefficient is determined based on the correlation between the product keywords corresponding to the reference behavior feature and the historical product keywords corresponding to the historical user behavior feature, to determine whether the user behavior feature is a clearly pointed category, and then adaptively select the product push method to improve the accuracy of pushing products to users.

[0047] In particular, for clearly targeted categories, consider locking in reference keywords, and determining derivative keywords based on the correlation between the keywords of each product. Considering other potential products for purchase, push product pages based on the derivative keywords, and improve the accuracy of product push to users while ensuring data reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic diagram of the steps of the e-commerce platform data analysis method according to an embodiment of the invention;

[0049] Figure 2 A logic diagram for determining whether a user behavior feature is a reference behavior feature according to an embodiment of the present invention;

[0050] Figure 3 A logical diagram of the categories for determining user behavior characteristics according to an embodiment of the present invention;

[0051] Figure 4 This is a logical diagram of pushing products to users according to an embodiment of the invention. DETAILED DESCRIPTION

[0052] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0054] See also Figures 1 to 4 As shown, Figure 1 This is a schematic diagram of the steps of the e-commerce platform data analysis method according to an embodiment of the present invention. Figure 2This is a logic diagram for determining whether a user behavior feature is a reference behavior feature according to an embodiment of the present invention. Figure 3 This is a logical diagram of determining the category of user behavior characteristics according to an embodiment of the present invention. Figure 4 This is a logical diagram of pushing products to users according to an embodiment of the present invention. The e-commerce platform data analysis method according to an embodiment of the present invention includes:

[0055] Create a user behavior database and a product information database. The user behavior database is used to obtain and store user behavior characteristics in real time, and the product information database is used to store the association relationship between each product keyword.

[0056] The user behavior characteristics include a product page refresh frequency and a product page pull completeness for a single product page, and the association relationship is determined based on a plurality of user behavior characteristics stored in the user behavior database;

[0057] Calculating a feature highlighting representation value for the user behavior feature based on the user's user behavior feature to determine whether the user behavior feature is a reference behavior feature, and determining a correlation consistency coefficient based on the correlation between the product keyword corresponding to the reference behavior feature and the historical product keyword corresponding to the user's historical user behavior feature to determine the category that the user behavior feature points to;

[0058] Pushing products to users based on the categories of user behavior characteristics, including:

[0059] Based on the product page corresponding to the user behavior feature, a reference keyword is locked, several derivative keywords associated with the reference keyword are determined, several product pages containing the derivative keywords are determined, and a push frequency for each of the product pages is determined based on the feature highlighting representation value, so as to push the product page to the user;

[0060] Alternatively, the user's historical user behavior characteristics are randomly screened to determine the corresponding product page, extract product keywords, and push the product page containing the product keywords to the user.

[0061] Specifically, there is no limitation on the specific method of constructing the user behavior database. After obtaining the user's authorization, relevant information about the user browsing the product interface can be collected to obtain the user behavior characteristics, which will not be elaborated here.

[0062] Specifically, the process of determining the association relationship between product keywords includes:

[0063] Calling the user behavior characteristics of several users to determine the product keywords in the user's single browsing of several product pages;

[0064] Calculate the co-occurrence probability of a single product keyword and the remaining product keywords in a user's single browsing;

[0065] If the co-occurrence probability between a single product keyword and any product keyword is greater than a predetermined co-occurrence probability threshold, it is determined that there is an association relationship between the single product keyword and any product keyword;

[0066] Among them, a single browsing is from the time when the user starts browsing the product page through the platform to the time when the user closes the platform.

[0067] The predetermined co-occurrence probability threshold is pre-set, wherein the user behavior characteristics of several users are statistically analyzed to determine the co-occurrence probability of different product keywords in a single browsing of the user, the co-occurrence probability is used as a random variable to construct a probability density function to determine a normal distribution curve, the co-occurrence probability corresponding to the midpoint of the 95% confidence interval is determined, and 0.75 to 0.85 times the co-occurrence probability is used as the predetermined co-occurrence probability threshold.

[0068] It is understandable that the platform side can be any e-commerce platform that provides e-commerce services. When the user side purchases goods, it logs in to the e-commerce platform, browses the product page, and closes the e-commerce platform after completing the browsing. This will not be elaborated here.

[0069] The present invention takes into account the association relationship between several keywords in each product page covered by the platform, and determines other products that the user may have potential purchasing tendencies through the association relationship. In actual situations, the user's user behavior can reflect whether the user has potential associated purchasing tendencies for the product. By identifying the keywords in each product page to build an association relationship, after subsequently locking the product that the user currently has potential associated purchasing tendencies, support is provided for identifying the products corresponding to other product keywords that have an association relationship with the product keywords of the above-mentioned product, so as to facilitate accurate product push to users.

[0070] Specifically, the process of calculating the feature highlighting representation value for the user behavior characteristics based on the user's user behavior characteristics includes:

[0071] Calculate the ratio of the product page refresh frequency to the preset refresh frequency threshold as the first feature highlighting coefficient;

[0072] The completeness of the product page is used as the second feature highlighting coefficient;

[0073] A feature salient characterization value is obtained by weightedly summing the first feature salient characterization coefficient and the second feature salient characterization coefficient.

[0074] In this embodiment, when performing weighted summation, the weight of the first feature salient characterization coefficient is set to 0.6, and the weight of the second feature salient characterization coefficient is set to 0.4;

[0075] The refresh frequency threshold P0 is pre-determined by obtaining historical user data on the platform, extracting the refresh frequency of the product page, and calculating the average refresh frequency of the product page. The refresh frequency threshold is set to between 1.15 and 1.2 times the average refresh frequency.

[0076] The present invention calculates the feature-highlighting characterization value for the user behavior feature based on the user's user behavior feature. In actual situations, due to the unclear directionality of a single behavior, it is impossible to clearly determine the user's purchasing intention, which may lead to inaccurate content pushed by the platform. The present application calculates the feature-highlighting characterization value for the user behavior feature to characterize the degree of potential purchasing intention reflected by the user behavior, and determines the user behavior feature with clarity as the reference behavior feature. Furthermore, in order to avoid the low accuracy of the data represented by the reference behavior feature alone, the correlation consistency coefficient is determined based on the correlation between the product keywords corresponding to the reference behavior feature and the historical product keywords corresponding to the historical user behavior feature, to determine whether the user behavior feature is a clearly pointed category, and then adaptively select the product push method to improve the accuracy of pushing products to users.

[0077] Specifically, the area covered by sliding from the top of the product page to the end of the page is determined as the total area of ​​the product page, and the ratio of the area covered by sliding when the user pulls the product page to the total area of ​​the product page is determined as the completeness of the product page pull.

[0078] Specifically, determining whether the user behavior feature is a reference behavior feature includes:

[0079] If the feature salience value is greater than or equal to the feature salience value threshold, the user behavior feature is determined to be a reference behavior feature;

[0080] If the feature salience representation value is less than the feature salience representation value threshold, it is determined that the user behavior feature is not a reference behavior feature.

[0081] The feature highlighting value threshold F0 is selected in the interval [0.75, 0.95].

[0082] Specifically, the process of determining the correlation consistency coefficient includes,

[0083] Determine the product page corresponding to the user's reference behavior characteristics, and extract product keywords from the product page;

[0084] Determining historical user behavior characteristics of the user within a predetermined period in the user behavior database, and extracting historical product keywords from product pages;

[0085] Determining the average correlation between each of the product keywords and the historical product keywords;

[0086] The association degree mean is determined as the association consistency coefficient.

[0087] Specifically, the correlation between product keywords is determined based on historical user behavior characteristics, and the co-occurrence probability of two product keywords in a single browsing in the historical user behavior characteristics is determined as the correlation.

[0088] Specifically, determining the category of the user behavior characteristics includes:

[0089] If the correlation consistency coefficient is greater than or equal to the correlation consistency coefficient threshold, the user behavior feature is determined to be a clear pointing category;

[0090] If the correlation consistency coefficient is less than the correlation consistency coefficient threshold, the user behavior feature is determined to be a fuzzy pointing category.

[0091] The association consistency coefficient threshold is determined based on a predetermined co-occurrence probability threshold, and the association consistency coefficient threshold is set to 0.75 times the predetermined co-occurrence probability threshold.

[0092] Specifically, the product push to the user is carried out based on the category of the user behavior characteristics, including:

[0093] If the user behavior feature clearly points to a category, then based on the product page corresponding to the user behavior feature, a reference keyword is locked, several derivative keywords associated with the reference keyword are determined, several product pages containing the derivative keywords are determined, and a push frequency for each of the product pages is determined based on the feature prominence representation value, so as to push the product page to the user;

[0094] If the user behavior feature is a fuzzy pointing category, the user's historical user behavior features are randomly screened, the corresponding product page is determined to extract product keywords, and the product page containing the product keywords is pushed to the user.

[0095] Specifically, the process of locking reference keywords on the product page corresponding to the user behavior characteristics includes:

[0096] Determining the product page corresponding to the user behavior characteristics;

[0097] A number of product keywords are extracted from the product page, and the frequency of occurrence of each product keyword is calculated, and the product keyword corresponding to the highest frequency is locked as a reference keyword.

[0098] It is understandable that the product page contains several product keywords. The product keyword that appears most frequently on the product page is obtained and locked as the reference keyword, which will not be repeated here.

[0099] Specifically, the process of determining several derived keywords associated with the reference keyword includes:

[0100] Retrieving from the product information database several other product keywords that are associated with the reference keyword;

[0101] The called commodity keywords are determined as derived keywords.

[0102] For clearly targeted categories, consider locking in reference keywords, and determining derivative keywords based on the correlation between the keywords of each product. Consider other potential products for purchase, and push product pages based on the derivative keywords. While ensuring data reliability, improve the accuracy of product push to users.

[0103] Specifically, the push frequency for each product page is determined based on the feature highlighting value, including:

[0104] The push frequency for each of the product pages is increased, and the increase in push frequency is positively correlated with the feature highlighting representation value.

[0105] In this embodiment, optionally,

[0106] The feature salient characterization value F is compared with the first feature salient characterization value comparison threshold F1 and the second feature salient characterization value comparison threshold F2,

[0107] If F>F2, the push frequency increase amount is determined to be the first push frequency increase amount h1, and h1=0.42h0;

[0108] If F1≤F≤F2, the push frequency increase amount is determined to be the second push frequency increase amount h2, and h2=0.37h0;

[0109] If F<F1, the push frequency increase amount is determined to be the third push frequency increase amount h3, and h3 is set to 0.26h0;

[0110] Among them, h0 represents the initial push frequency, F1 = 1.2F0, F2 = 1.4F0.

[0111] It is understandable that the initial push frequency can be determined based on the total amount of goods on the platform and the sales of goods, which will not be elaborated here.

[0112] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for analyzing e-commerce platform data, characterized in that: include: Create a user behavior database and a product information database. The user behavior database is used to obtain and store user behavior characteristics in real time, and the product information database is used to store the association relationship between each product keyword. The user behavior characteristics include a product page refresh frequency and a product page pull completeness for a single product page, and the association relationship is determined based on a plurality of user behavior characteristics stored in the user behavior database; Calculating a feature highlighting representation value for the user behavior feature based on the user's user behavior feature to determine whether the user behavior feature is a reference behavior feature, and determining a correlation consistency coefficient based on the correlation between the product keyword corresponding to the reference behavior feature and the historical product keyword corresponding to the user's historical user behavior feature to determine the category that the user behavior feature points to; Pushing products to users based on the categories of user behavior characteristics, including: If the user behavior feature clearly points to a category, a reference keyword is locked based on the product page corresponding to the user behavior feature, several derivative keywords associated with the reference keyword are determined, several product pages containing the derivative keywords are determined, and a push frequency for each of the product pages is determined based on the feature prominence representation value, so as to push the product page to the user; If the user behavior feature is a fuzzy pointing category, randomly screen the user's historical user behavior features, determine the corresponding product page, extract product keywords, and push the product page containing the product keywords to the user; Determine the category of the user behavior characteristics, including: If the correlation consistency coefficient is greater than or equal to the correlation consistency coefficient threshold, the user behavior feature is determined to be a clear pointing category; If the correlation consistency coefficient is less than the correlation consistency coefficient threshold, the user behavior feature is determined to be a fuzzy pointing category.

2. The e-commerce platform data analysis method according to claim 1, characterized in that: The process of determining the association relationship between product keywords includes: Calling the user behavior characteristics of several users to determine the product keywords in the user's single browsing of several product pages; Calculate the co-occurrence probability of a single product keyword and the remaining product keywords in a user's single browsing; If the co-occurrence probability between a single product keyword and any product keyword is greater than a predetermined co-occurrence probability threshold, it is determined that there is an association relationship between the single product keyword and any product keyword; Among them, a single browsing is from the time when the user starts browsing the product page through the platform to the time when the user closes the platform.

3. The e-commerce platform data analysis method according to claim 1, characterized in that: The process of calculating the feature highlighting representation value for the user behavior characteristics based on the user's user behavior characteristics includes: Calculate the ratio of the product page refresh frequency to the preset refresh frequency threshold as the first feature highlighting coefficient; The completeness of the product page is used as the second feature highlighting coefficient; A feature salient characterization value is obtained by weightedly summing the first feature salient characterization coefficient and the second feature salient characterization coefficient.

4. The e-commerce platform data analysis method according to claim 1, characterized in that: Determining whether the user behavior feature is a reference behavior feature includes: If the feature salience representation value is greater than or equal to the feature salience representation value threshold, the user behavior feature is determined to be a reference behavior feature.

5. The e-commerce platform data analysis method according to claim 1, characterized in that: The process of determining the correlation coefficient includes, Determine the product page corresponding to the user's reference behavior characteristics, and extract product keywords from the product page; Determining historical user behavior characteristics of the user within a predetermined period in the user behavior database, and extracting historical product keywords from product pages; Determining the average correlation between each of the product keywords and the historical product keywords; The association degree mean is determined as the association consistency coefficient.

6. The e-commerce platform data analysis method according to claim 1, characterized in that: The process of locking reference keywords on the product page based on the user behavior characteristics includes: Determining the product page corresponding to the user behavior characteristics; A number of product keywords are extracted from the product page, and the frequency of occurrence of each product keyword is calculated, and the product keyword corresponding to the highest frequency is locked as a reference keyword.

7. The e-commerce platform data analysis method according to claim 1, characterized in that ,The process of determining several derivative keywords that have an association relationship with the ,reference keyword includes: Retrieving from the product information database several other product keywords that are associated with the reference keyword; The called commodity keywords are determined as derived keywords.

8. The e-commerce platform data analysis method according to claim 1, characterized in that: Determining the push frequency for each product page based on the feature highlighting representation value includes: The push frequency for each of the product pages is increased, and the increase in push frequency is positively correlated with the feature highlighting representation value.

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