Authors: Jin Min Yang, Kin Fun Li, Da Fang Zhang
Addresses: Software School, Hunan University, Changsha 410082, China. ' Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 3P6, Canada. ' Software School, Hunan University, Changsha 410082, China
Abstract: Collaborative filtering provides personalised recommendations based on individual user preferences as well as those of other users with similar interests. In collaborative filtering, memory-based approaches make predictions by measuring the whole similarity between two users. When a user has multiple interest genres, those methods seem too optimistic in making correct predictions in some situations. In addition, minor genres are often inhibited due to their minute share of the whole similarity. In this paper, we present a novel approach that combines the advantages of item-item similarity and user-user similarity by introducing a genre component to the relation between user and item. In our approach, the direct user-item relevance is developed into a combination of genre similarity and preference similarity, thus capturing more accurately the relevance between items as well as between user and item. Experimental results from EachMovie and MovieLens datasets show that our approach outperforms four other state-of-the-art collaborative filtering algorithms.
Keywords: collaborative filtering; interest genres; relevance measurement; prediction accuracy; adaptive filtering; personalised recommendations; individual user preferences; similar interests; genre similarity; preference similarity.
International Journal of Communication Networks and Distributed Systems, 2008 Vol.1 No.2, pp.216 - 230
Published online: 10 Sep 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article