Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in r...
Robin D. Burke, Bamshad Mobasher, Chad Williams, R...
This paper discusses the combination of collaborative and contentbased filtering in the context of web-based recommender systems. In particular, we link the well-known MovieLens ...
Abstract. We propose that traditional case-based recommender systems can be improved by informing them with context data describing the user's environment. We outline existing...
Recommender Systems are gaining widespread acceptance in e-commerce applications to confront the information overload problem. Collaborative Filtering (CF) is a successful recommen...
We describe a minimalist methodology to develop usage-based recommender systems for multimedia digital libraries. A prototype recommender system based on this strategy was impleme...
Johan Bollen, Michael L. Nelson, Gary Geisler, Raq...