View-based recommender systems

Different recommender systems based on collaborative technology have been proposed that recommend new relevant products to users by exploring past user preference patterns. The most common approach generates recommendations based on user consumption patterns and on rating information gathered during each user-item interaction. In this paper we introduce a novel approach based on views of items, which are basically more complex models of the user-item interactions aimed at capturing the aspects on which users base their ratings. The resulting view-based approach recommends views to users instead of the traditional items. The proposed algorithms are tested on an artificial database, and the results show that modeling further interaction information improves the accuracy of predictions, provides a robust background to explain recommendations, exposes users to more specific recommendations and leads to better models of user preferences.