Detection of the customer time-variant pattern for improving recommender systems

摘要

Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems—content-based recommending and collaborative filtering. So far, collaborative filtering recommender systems have been very successful in both information filtering and e-commerce domains. However, the current research on recommendation has paid little attention to the use of time-related data in the recommendation process. Up to now there has not been any study on collaborative filtering to reflect changes in user interest.This paper suggests a methodology for detecting a user's time-variant pattern in order to improve the performance of collaborative filtering recommendations. The methodology consists of three phases of profiling, detecting changes, and recommendations. The proposed methodology detects changes in customer behavior using the customer data at different periods of time and improves the performance of recommendations using information on changes.