A knowledge reuse framework for improving novelty and diversity in recommendations

Abstract

Recommender system (RS) is an important instrument in e-commerce, which provides personalized recommendations to individual user. Classical algorithms in recommender system mainly emphasize on recommendation accuracy in order to match individual user's past profile. However, recent study shows that novelty and diversity in recommendations are equally important factors from both user and business view points. In this paper, we introduce a knowledge reuse framework to increase novelty and diversity in the recommended items of individual users while compromising very little recommendation accuracy. The proposed framework uses features information which have already been extracted by an existing collaborative filtering. Experimental results with real datasets show that our approach outperforms state-of-the-art solutions in providing novel and diverse recommended items to individual users and aggregate diversity gain achieved by our approach is on par with recently proposed rank based approach.