Collaborative Filtering Recommendation Based on Dynamic Changes of User Preferences

doi:10.3969/j.issn.1006-2475.2020.01.014

Abstract

Abstract: Traditional collaborative filtering methods focus only on rating data to generate recommendation， without considering the evaluation time， project category and other informations, which affects the quality of the recommended system. This paper proposes a personalized recommendation model based on the dynamic changes of user preferences. The method is based on the project category, and different weighting functions are set up according to the user scoring time (recent, long and periodic). The experimental results from Movielens data set show that this method weakens the influence of short-term preference on recommendationquality, reflects the dynamic changes of user preferences accurately, and improves the accuracy of recommendation effectively.

Key words: collaborative filtering, dynamic changes of user preferences, evaluation time, item category

CLC Number:

TP311

JIANG Shu-hao1,2, ZHANG Li-yi1,2, ZHOU Na1. Collaborative Filtering Recommendation Based on Dynamic Changes of User Preferences[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.01.014.

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