CN115330476A - Cross-border retail imported commodity intelligent recommendation system based on big data - Google Patents
Cross-border retail imported commodity intelligent recommendation system based on big data Download PDFInfo
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- CN115330476A CN115330476A CN202210686696.2A CN202210686696A CN115330476A CN 115330476 A CN115330476 A CN 115330476A CN 202210686696 A CN202210686696 A CN 202210686696A CN 115330476 A CN115330476 A CN 115330476A
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Abstract
The invention discloses a big data-based cross-border retail imported commodity intelligent recommendation system, which comprises a data acquisition module, an attribute module, an analysis module, a recommendation module and a data updating module, wherein the data acquisition module is used for acquiring a plurality of data; after the data acquisition module acquires the user image, the attribute module analyzes the user image to acquire the characteristic information of the user; the characteristic information of the user comprises the attribute of the imported commodity; the import and export commodity attribute is the record of whether the user purchases or browses and clicks the import and export commodities; the analysis module classifies users with imported commodity attributes and analyzes the specific commodity categories of the users to obtain recommended imported commodity categories corresponding to the users; and the recommending module recommends corresponding commodities to the user according to the purchase history data of the user. According to the commodity recommendation method and device, the attention degree of the user is calculated according to the collected user image, the corresponding commodity is automatically pushed according to the attention degree value, the data are prevented from being manually set by the user, and the intelligence and the applicability of commodity recommendation are improved.
Description
Technical Field
The invention relates to the technical field of cross-border e-commerce, in particular to a cross-border retail imported commodity intelligent recommendation system based on big data.
Background
In recent years, with the development of the internet industry, cross-border electronic commerce is more and more popular, and is an international business activity that deals with different customs and achieves transactions, performs payment and settlement through an electronic commerce platform, and delivers commodities through cross-border logistics to complete transactions. Meanwhile, with the development of electronic commerce becoming more and more rapid, accurate recommendation for cross-border e-commerce also becomes a necessary marketing mode for a cross-border e-commerce platform. In practical applications, cross-border retail goods usually rely on domestic marketing platforms, but have no dedicated recommendation system, so that consumers of cross-border goods have certain brand loyalty and are relatively willing to try new products; in addition, the variety of commodities in cross-border retail is various, and the user data and the commodity data can be acquired and processed based on the big data technology, so that a recommendation system suitable for cross-border retail imported commodities based on big data is needed.
Disclosure of Invention
1. The technical problem to be solved is as follows:
aiming at the technical problems, the invention provides the cross-border retail imported commodity intelligent recommendation system based on the big data, which is used for generating the user image by collecting the characteristics of the user through the big data technology and realizing accurate recommendation according to the user image and the historical consumption record of the user.
2. The technical scheme is as follows:
the utility model provides a cross border retail import commodity intelligence recommendation system based on big data which characterized in that: the system comprises a data acquisition module, an attribute module, an analysis module, a recommendation module and a data updating module;
after the data acquisition module acquires the user image, the attribute module analyzes the user image to acquire the characteristic information of the user; the characteristic information of the user comprises the attribute of the imported commodity; the import and export commodity attribute is the record of whether the user purchases or browses and clicks the import and export commodity; the analysis module classifies users with imported commodity attributes and analyzes the specific commodity categories to obtain recommended imported commodity categories corresponding to the users; the recommending module recommends corresponding commodities to the user according to the purchase history data of the user;
the data updating module can update the user image and the commodity information according to the historical data generated in the new period by the preset updating period.
Further, the user image is obtained by mining the interest and preference characteristics of the user through user behavior log information.
Further, the recommendation method of the recommendation system specifically comprises the following steps:
the method comprises the following steps: acquiring a user image, and analyzing whether characteristic information in the user image contains in-out commodity attributes or not; if the step II is included, performing the step II; if not, performing the third step;
step two: classifying the categories of the import and export commodities according to a preset classification method, and attaching corresponding commodity category labels to the users;
step three: according to the interest of the user, the corresponding commodity is preferred, and a corresponding commodity category label is attached to the user;
step four: calculating the attention value corresponding to the category commodity for the user commodity category label according to the record of purchasing or browsing; the recommendation module recommends commodities of categories corresponding to the attention values of the commodities according to a preset sequence; the attention value A is as follows:
a = a number of purchases + β number of clicks total time of browsing;
in the formula, alpha and beta are preset coefficients.
Further, the fourth step includes acquiring a purchase history of the user, and recommending the goods of the corresponding category according to a time interval of the purchase history.
Further, in step four, for the user without import goods feature, the recommending module recommends the goods category closest to the goods purchased or browsed by the user.
3. Has the advantages that:
the invention directly analyzes the user image generated by big data, and the user image is divided into two attributes: one that has a record of having purchased or clicked on imported goods, one that has not; and labels of the categories of the corresponding commodities are attached to the user according to two different conditions, the attention degree of the user to the commodities of the categories is calculated, and the sequence of pushing import and export commodities is determined according to the numerical value of the attention degree. According to the method and the device, the attention degree of the user is calculated according to the acquired user image, and the corresponding commodity is automatically pushed according to the attention degree value, so that the data are prevented from being manually set by the user, and the intelligence and the applicability of commodity recommendation are improved.
Drawings
FIG. 1 is a flow chart of a system employing the present invention.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
The utility model provides a cross border retail import commodity intelligence recommendation system based on big data which characterized in that: the system comprises a data acquisition module, an attribute module, an analysis module, a recommendation module and a data updating module;
after the data acquisition module acquires the user image, the attribute module analyzes the user image to acquire the characteristic information of the user; the characteristic information of the user comprises the attribute of the imported commodity; the import and export commodity attribute is a record of whether the user purchases or browses and clicks import and export commodities; the analysis module classifies users with imported commodity attributes and analyzes the specific commodity categories of the users to obtain recommended imported commodity categories corresponding to the users; the recommending module recommends corresponding commodities to the user according to the purchase history data of the user;
the data updating module can update the user image and the commodity information according to the historical data generated in the new period by the preset updating period.
Further, the user image is obtained by mining the interest and preference characteristics of the user through user behavior log information.
As shown in fig. 1, further, the recommendation method of the recommendation system specifically includes the following steps:
the method comprises the following steps: acquiring a user image, and analyzing whether characteristic information in the user image contains in-out commodity attributes or not; if the step II is included, performing the step II; if not, performing the third step;
step two: classifying the categories of import and export commodities according to a preset classification method, and attaching corresponding commodity category labels to the users;
step three: according to the interest of the user, the corresponding commodity is preferred, and a corresponding commodity category label is attached to the user;
step four: calculating the attention value corresponding to the category commodity for the user commodity category label according to the record of purchasing or browsing; the recommendation module recommends commodities of categories corresponding to the attention values of the commodities according to a preset sequence; the attention value A is as follows:
a = a number of purchases + β number of clicks total time of browsing;
in the formula, alpha and beta are preset coefficients.
Further, the fourth step includes acquiring a purchase history of the user, and recommending the goods of the corresponding category according to a time interval of the purchase history.
Further, in step four, for the user without import goods feature, the recommending module recommends the goods category closest to the goods purchased or browsed by the user.
The specific embodiment is as follows:
as shown in fig. 1, the recommendation system directly uses the user image generated by big data, and simply classifies the features of the user data: one with records of purchases or clicks on imported goods, one without; this relatively simple processing of the data can reduce the computational load on the system. And labeling the category label of the corresponding commodity to the user according to two different conditions, calculating the attention degree of the user to the category commodity, and determining the sequence of pushing the import and export commodities according to the numerical value of the attention degree. According to the method and the device, the attention degree of the user is calculated according to the acquired user image, and the corresponding commodity is automatically pushed according to the attention degree value, so that the data are prevented from being manually set by the user, and the intelligence and the applicability of commodity recommendation are improved.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. The utility model provides a cross border retail import commodity intelligence recommendation system based on big data which characterized in that: the system comprises a data acquisition module, an attribute module, an analysis module, a recommendation module and a data updating module;
after the data acquisition module acquires the user image, the attribute module analyzes the user image to acquire the characteristic information of the user; the characteristic information of the user comprises the attribute of the imported commodity; the import and export commodity attribute is the record of whether the user purchases or browses and clicks the import and export commodity; the analysis module classifies users with imported commodity attributes and analyzes the specific commodity categories to obtain recommended imported commodity categories corresponding to the users; the recommending module recommends corresponding commodities to the user according to the purchase history data of the user;
the data updating module can update the user image and the commodity information according to the historical data generated in the new period by the preset updating period.
2. The big data based intelligent recommendation system for cross-border retail imported goods according to claim 1, wherein: the user image is obtained by mining the interest and preference characteristics of the user through user behavior log information.
3. The big data based intelligent recommendation system for cross-border retail imported goods according to claim 1, wherein: the recommendation method of the recommendation system specifically comprises the following steps:
the method comprises the following steps: acquiring a user image, and analyzing whether characteristic information in the user image contains in-out commodity attributes or not; if the step II is included, performing the step II; if not, performing the third step;
step two: classifying the categories of import and export commodities according to a preset classification method, and attaching corresponding commodity category labels to the users;
step three: according to the interest of the user, the corresponding commodity is preferred, and a corresponding commodity category label is attached to the user;
step four: calculating the attention value corresponding to the category commodity for the user commodity category label according to the record of purchasing or browsing; the recommending module recommends commodities of categories corresponding to the attention values of the commodities according to a preset sequence; the attention value A is as follows:
a = α × number of purchases + β × number of clicks × total time of browsing;
in the formula, alpha and beta are preset coefficients.
4. The big data based intelligent recommendation system for cross-border retail imported goods according to claim 3, wherein: and step four, acquiring the purchase history of the user, and recommending the commodities of the corresponding category according to the time interval of the purchase history.
5. The big data based intelligent recommendation system for cross-border retail imported goods according to claim 3, wherein: in the fourth step, for the users without import commodity characteristics, the recommending module recommends the commodity category closest to the commodity purchased or browsed by the user.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117217865A (en) * | 2023-09-12 | 2023-12-12 | 深圳市思维无限网络科技有限公司 | Personalized recommendation system based on big data analysis |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117217865A (en) * | 2023-09-12 | 2023-12-12 | 深圳市思维无限网络科技有限公司 | Personalized recommendation system based on big data analysis |
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