Cold-start recommendation method based on user preferences and trust

Abstract

The invention discloses a cold-start recommendation method based on user preferences and trust. The method comprises the steps of S1, measuring comprehensive trust values between users according to social information of the users, and constructing a trust relation matrix; S2, calculating preference similarity degrees of the users according to user scoring data, and constructing a preference relation matrix; S3, utilizing a calculation method of comprehensive similarity degrees to fuse preference relations and trust relations, and using a bee colony algorithm to iteratively update weights in the comprehensive similarity degrees, carrying out multi-objective optimization to enable the weights to become optimal in a self-adaptive manner, and constructing a preference trust relation matrix; S4, selecting a most-trusted neighbour set of the target user to predict scoring values of corresponding items for the target user on the basis of the preference trust relation matrix; and S5, recommending the items with high prediction scores to the target user. According to the method, the precision of user trust measuring is improved, the user behavior preferences are more accurately constructed, and the quality of recommendation for the cold-start user is improved.

Description

technical field [0001] The invention belongs to the field of data mining collaboration, in particular to a cold start recommendation method based on user preference and trust. Background technique [0002] The recommendation system refers to the application of knowledge discovery technology to generate personalized recommendations, thereby helping users to filter out useful information from a large number of articles, products, movies, music, web pages, etc., and has been widely used in various e-commerce platforms. [0003] The existing recommendation methods are mainly divided into three types: content-based recommendation, association rule-based recommendation, content filtering recommendation, and collaborative filtering recommendation. In particular, collaborative filtering recommendation technology is the most widely used recommendation method. However, collaborative filtering recommendation technology also has the problem of user cold start. The principle of the col...

AI Technical Summary

Problems solved by technology

However, the uniqueness of the two types of data is often overlooked, so there are two problems when dealing with the two types of data: On the one hand, the trust relationship of users is generally only considered from a single trust perspective, that is, only considering The impact of a single aspect on user trust, and the general similarity calculation method is used for user rating similarity; on the other hand, in the calculation of user comprehensive similarity, traditional methods are used to determine the weight of user rating similarity, trust value, and social similarity. Value distribution, resulting in a certain one-sidedness in the accuracy of the user's comprehensive similarity

Therefore, the existing user cold-start recommendation method that integrates trust information and rating information still fails to effectively improve the accuracy of personalized recommendation for cold users

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