Personalized suggestions by means of Collaborative Filtering: A comparison of two different model-based techniques

Recommendation systems are commonly used for suggesting products or services. Among different existing techniques, Model-Based Collaborative Filtering (MBCF) approaches have been proven to address scalability and cold-starting problems that often arise. In this paper we investigate two MBCF algorithms: Self-Organizing Maps (SOM) for Collaborative Filtering and Item-based Fuzzy Clustering Collaborative Filtering (IFCCF). These two techniques have been selected because preliminary results have proven that when applied to the clustering of users or items the quality of the recommendation system increases with respect to the k-means. Within recommendation systems, no comparison of these two techniques exists. Therefore, our experimentation is aimed at comparing these two techniques by means of MovieLens and Jester dataset in order to provide a guideline for their implementation in the e-Commerce domain.