Personalized commodity recommendation method

Abstract

The invention relates to a personalized commodity recommendation method. The method comprises the steps of receiving a commodity search instruction of a user; If the users are classified, calculatingmulti-dimensional average label information preferred by the users, wherein a recommendation model of the users calculates first multi-dimensional attribute information based on the multi-dimensionalaverage label information, calculates the distance between the first multi-dimensional attribute information and second multi-dimensional attribute information of each commodity in the commodity category, and pushes the commodities with the smaller distance to the users; Otherwise, calculating first multi-dimensional average attribute information based on the second multi-dimensional attribute information of the plurality of commodities, and determining a recommendation model suitable for the user based on the first multi-dimensional average attribute information and the recommendation model of each type of users. According to the recommendation method provided by the invention, firstly, users are clustered, and multi-dimensional commodity label information and commodity attribute information are utilized, so that the recommendation accuracy is high; Besides, when the user is a new user, a recommendation model of the new user is formed by utilizing an existing recommendation model through attention transfer learning, so that the cold start problem is solved.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to a method for recommending personalized commodities. Background technique [0002] With the continuous development of network technology, in today's era of information overload, both as information consumers and information producers have encountered great challenges: information consumers need to find the information they are interested in from the massive amount of information; Information producers need to make their information stand out and attract users' attention. The personalized recommendation method can solve the above problems very well. At present, the main personalized recommendation methods mainly include content-based recommendation methods and collaborative filtering recommendation methods. However, these methods generally have problems such as low accuracy and cold start. Contents of the invention [0003] The present invention provides a method for recomm...

AI Technical Summary

Problems solved by technology

[0003] The present invention provides a personalized product recommendation method to solve the problem of low recommendation accuracy existing in the prior art

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