Item-Based and User-Based Incremental Collaborative Filtering for Web Recommendations

In this paper we propose an incremental item-based collaborative filtering algorithm. It works with binary ratings (sometimes also called implicit ratings), as it is typically the case in a Web environment. Our method is capable of incorporating new information in parallel with performing recommendation. New sessions and new users are used to update the similarity matrix as they appear. The proposed algorithm is compared with a non-incremental one, as well as with an incremental user-based approach, based on an existing explicit rating recommender. The use of techniques for working with sparse matrices on these algorithms is also evaluated. All versions, implemented in R, are evaluated on 5 datasets with various number of users and/or items. We observed that: Recall tends to improve when we continuously add information to the recommender model; the time spent for recommendation does not degrade; the time for updating the similarity matrix (necessary to the recommendation) is relatively low and motivates the use of the item-based incremental approach. Moreover we study how the number of items and users affects the user based and the item based approaches.