A folksonomy-based recommender system for personalized access to digital artworks
Journal on Computing and Cultural Heritage (JOCCH)
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Recommenders systems are used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based ones recommend items similar to those a given user has liked in the past. Indeed, the past behavior is supposed to be a reliable indicator of her future behavior. This assumption, however, causes the overspecialization problem. Our purpose is to mitigate the problem stimulating users and facilitating the serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a content-based approach and a serendipitous heuristic in order to provide also surprising suggestions. The reference scenario concerns with personalized tours in a museum and serendipitous items are introduced by slight diversions on the context-aware tours.