Comparing Pre-filtering and Post-filtering Approach in a Collaborative Contextual Recommender System: An Application to E-Commerce

Authors:
Umberto Panniello;Michele Gorgoglione;Cosimo Palmisano
Affiliations:
Department of mechanical and business engineering, Polytechnic of Bari, Bari, Italy;Department of mechanical and business engineering, Polytechnic of Bari, Bari, Italy;Aizoon consulting, Senior consultant at Fiat Group Automobiles, Turin, Italy
Venue:
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Year:
2009

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Abstract

Recent literature predicts that including context in a recommender system may improve its performance. The context-based recommendation approaches are classified as pre-filtering, post-filtering and contextual modeling. Little research has been done on studying whether including context in a recommender system improves the recommendation performance and no research has compared yet the different approaches to contextual RS. The research contribution of this work lies in studying the effect of the context on the recommendation performance and comparing a pre-filtering approach to a post-filtering using a collaborative filtering recommender system.