We examine the case of over-specialization in recommender systems, which results from returning items that are too similar to those previously rated by the user. We propose Outsid...
Zeinab Abbassi, Sihem Amer-Yahia, Laks V. S. Laksh...
We propose a novel collaborative recommendation approach to take advantage of the information available in user-created lists. Our approach assumes associations among any two item...
Besides the rating information, an increasing number of modern recommender systems also allow the users to add personalized tags to the items. Such tagging information may provide...
Modern techniques for distributed information retrieval use a set of documents sampled from each server, but these samples have been underutilised in server selection. We describe...
Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation [2, 10, 11, 17]. In ...