Commodity recommendation and recommendation relationship chain tracking method

Abstract

The invention relates to the field of e-commerce, in particular to a commodity recommendation and recommendation relationship chain tracking method, which comprises the following steps that: when a user enters a commodity detail interface for the first time, creating a recommendation record insertion recommendation table which is used for storing all recommendation records and tracking a recommendation relationship chain; wherein an upstream recommendation relationship chain in the recommendation records is generated according to a mode that the user enters a commodity detail interface. According to the method, only the user is allowed to recommend the commodity entering the detail interface, the user shares the recommendation link through the social platform to complete sharing recommendation, the recommendation link is a system recommendation processing program entrance. Wherein the recommendation ID parameter is used for identifying a specific recommendation record. According to themethod, only one upstream recommendation relationship chain for recommending the specific commodity to the specific user is ensured, the dispute among a plurality of recommendation chains for the same commodity of the same user is solved, the recommendation record cannot be changed once being created, and the solution of high-performance caching and high-concurrency processing based on the recommendation record is greatly simplified.

Description

technical field [0001] The invention relates to the field of electronic commerce, in particular to a method for product recommendation and recommendation relationship chain tracking. Background technique [0002] At present, the recommendation granularity of some member e-commerce systems that track users' multi-level recommendation relationship chains on the market is for the entire system rather than for different products in the system. The recommendation relationship is determined when the user registers rather than when the user first touches the product. It is determined that when the user purchases any product in the system, the upstream recommender has a corresponding profit share, and the fairness of the profit distribution is not reflected. However, some other e-commerce systems that record recommenders for product recommendation links do not track multi-level recommendation relationship chains. The recommendation relationship is determined at the time of purchase ...

AI Technical Summary

Problems solved by technology

The disadvantage of this model is that it is impossible to track the multi-level recommendation relationship chain, and users can only make recommendations within the first-level relationship circle. If you did not purchase the product through the referral link (purchased the product through other people's referral link or browsed by yourself), the referrer who actually introduced the product to the purchaser for the first time will not be able to get profit sharing

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