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...
A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative ...
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this ...
In this paper, we describe our work-in-progress with collaborative understanding of distributed ontologies in a multiagent framework. As reported earlier, the objective of this fr...
Significant appearance changes of objects under different orientations could cause loss of tracking, "drifting." In this paper, we present a collaborative tracking framew...