Commodity recommendation method and device

Abstract

The embodiment of the invention provides a commodity recommendation method and device. The commodity recommendation method comprises the steps: inputting a target user into a trained recommendation system, and obtaining a candidate commodity set; reordering each candidate commodity in the candidate commodity set based on a nearest neighbor search algorithm according to the target user representation vector and the target commodity representation vector, and taking a plurality of candidate commodities sorted in the top as optimal recommended commodities; and recommending all the optimal recommended commodities to the user. The commodity recommendation method and device generate the target user representation vector and the target commodity representation vector according to the similarity between the target user and each commodity, based on the recommendation system constructed by the graph neural network, so that the diversity of the candidate commodities is improved, and the diversityof the best recommended commodities is improved, and the commodity with the maximum probability is recommended to the user through the commodity recall stage, and the recommendation accuracy is improved.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a commodity recommendation method and device. Background technique [0002] Recommender system is an effective algorithm to solve the problem of information overload. Through the correlation modeling of user interaction history data and various features, the recommendation system can filter out products that meet the user's interests from a large number of candidate products. [0003] With the widespread deployment of recommendation systems in various Internet applications such as e-commerce, online news, and multimedia entertainment, the user experience measurement of recommendation systems has also expanded from a single accuracy index to include accuracy, diversity, novelty, and fairness. Comprehensive evaluation indicators of multiple dimensions such as sex. [0004] Among them, the diversity directly affects the user's perception of the recommendation results, an...

AI Technical Summary

Problems solved by technology

[0004] Among them, the diversity directly affects the user's perception of the recommendation results, and the lack of diversity of the recommendation results makes the products in the recommendation list appear redundant information, which reduces the effectiveness of the recommendation system

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