Alleviating Sparsity and Scalability Issues in Collaborative Filtering Based Recommender Systems

Abstract

Commercial recommender systems in general are used to evaluate very large product sets. In a user – item rating database, though users are very active, there are a few rating of the total number of items available. The user-item matrix is thus extremely sparse. Since a collaborative filtering algorithm is mainly based on similarity measures computed over the co-rated set of items, the large levels of sparsity can lead to less accuracy and can challenge the predictions or recommendations of the collaborative filtering (CF)systems. Further, a CF algorithm is assumed to be efficient if it is able to filter items that are interesting to users. But, they require computations that are very expensive and grow non-linearly with the number of users and items in a database. In general, the whole ratings database is searched in collaborative filtering and thus it suffers from poor scalability when more and more users and items are added into the database. Instigated by these challenges, we investigate two collaborative filtering algorithms, firstly an algorithm based on weighted slope one scheme and item clustering & secondly an algorithm based on item classification & item clustering, which deal with the sparsity and scalability issues simultaneously. Experiments were carried to determine which is better in terms of simplicity and accuracy among the two methods.