Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance

Abstract

Several approaches have been researched to help people deal with abundance of information. An important feature pioneered by social tagging systems and later used in other kinds of social systems is the ability to explore different community relevance prospects by examining items bookmarked by a specific user or items associated by various users with a specific tag. A ranked list of recommended items offered by a specific recommender engine can be considered as another relevance prospect. The problem that we address is that existing personalized social systems do not allow their users to explore and combine multiple relevance prospects. Only one prospect can be explored at any given time—a list of recommended items, a list of items bookmarked by a specific user, or a list of items marked with a specific tag. In this article, we explore the notion of combining multiple relevance prospects as a way to increase effectiveness and trust. We used a visual approach to recommend articles at a conference by explicitly presenting multiple dimensions of relevance. Suggestions offered by different recommendation techniques were embodied as recommender agents to put them on the same ground as users and tags. The results of two user studies performed at academic conferences allowed us to obtain interesting insights to enhance user interfaces of personalized social systems. More specifically, effectiveness and probability of item selection increase when users are able to explore and interrelate prospects of items relevance—that is, items bookmarked by users, recommendations and tags. Nevertheless, a less-technical audience may require guidance to understand the rationale of such intersections.