Unifying user-based and item-based collaborative filtering approaches by similarity fusion

Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large number of ratings from similar users or similar items are not available, due to the sparsity inherent to rating data. Consequently, prediction quality can be poor. This paper reformulates the memory-based collaborative filtering problem in a generative probabilistic framework, treating individual user-item ratings as predictors of missing ratings. The final rating is estimated by fusing predictions from three sources: predictions based on ratings of the same item by other users, predictions based on different item ratings made by the same user, and, third, ratings predicted based on data from other but similar users rating other but similar items. Existing user-based and item-based approaches correspond to the two simple cases of our framework. The complete model is however more robust to data sparsity, be...

• Type: Conference
• Year: 2006
• Where: SIGIR
• Authors: Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
• Keywords: Data Sparsity, SIGIR 2006, Similar Items, Similar Users