Commodity recommendation method and device, electronic commerce system, and storage medium

Abstract

The invention provides a commodity recommendation method and device, an electronic commerce system and a storage medium, and relate to the technical field of electronic commerce. The method comprises the steps: receiving a commodity recommendation request sent by a client, wherein the commodity recommendation request comprises a commodity class, first user position information, and the number N of target groups; screening out N target users based on the first user position information, wherein the commodities purchased by the target users comprise to-be-selected commodities matched with the commodity class; screening out the recommended commodities from the to-be-selected commodities according to a recommendation rule, and transmitting the commodity recommendation information corresponding to the recommended commodities to the client. According to the invention, the method achieves the personalized recommendation based on the number of groups nearby a user, and the user can dynamically select the recommendation range of groups. The method can obtain the commodities needed by the user more flexibly, improves the online shopping efficiency, and improves the purchase experience of the user.

Description

technical field [0001] The present invention relates to the technical field of e-commerce, in particular to a method and device for recommending commodities, an e-commerce system and a storage medium. Background technique [0002] For the current e-commerce manufacturers, the recommendation system has become one of the important ways to sell products. Merchants will recommend similar products according to the user's preferences, and users will choose products purchased by users with the same attributes as themselves. One of the key attributes of the same attributes is geographical location. It is found through research that users who are usually located in the same location range have great similarity in product preferences. In the existing recommendation system, the geographical range is selected as an important attribute to recommend the relevance of products. After the user logs in to the e-commerce company, the recommendation system recommends products based on the use...

AI Technical Summary

Problems solved by technology

However, after the geographical range is set, for example, within a range of 5km or 10km from the user, there may be no relevant shopping behaviors or orders generated within this geographical range, or the number of people who have shopping behaviors or generate orders is small, which is less than the user's expected The number of people who have shopping behavior or generated orders, resulting in the inability to get accurate recommendation results

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