전자상거래에서 2-Way 혼합 협력적 필터링을 이용한 추천 시스템

초록

Two defects have been pointed out in existing user-based collaborative filtering such as sparsity and scalability, and the research has been also made progress, which tries to improve these defects using item-based collaborative filtering. Actually, there were many results, but the problem of sparsity still remains because of being based on an explicit data. In addition, the issue has been pointed out, which attributes of item arenot reflected in the recommendation. This paper suggests a recommendation method using nave Bayesian algorithm in hybrid user and item-based collaborative filtering to improve above-mentioned defects of existing item-based collaborative filtering. This method generates a similarity table for each user and item, then it improves the accuracy of prediction and recommendation item using naive Bayesianalgorithm. It was compared and evaluated with existing item-based collaborative filtering technique to estimate the accuracy.