Time Weight Update Model Based on the Memory Principle in Collaborative Filtering

Abstract

Collaborative filtering is the most widely used technology in the recommender systems. Existing collaborative filtering algorithms do not take the time factor into account. However, users’ interests always change with time, and traditional collaborative filtering cannot reflect the changes. In this paper, the change of users’ interests is considered as the memory process, and a time weight iteration model is designed based on memory principle. For a certain user, the proposed model introduces the time weight for each item, and updates the weight by computing the similarity with the items chosen in a recent period. In the recommend process, the weight will be applied to the prediction algorithm. Experimental results show that the modified algorithm can optimize the result of the recommendation in a certain extent, and performs better than traditional collaborative filtering.