Knowledge Based Recommender Systems Using Explicit User Models

Brendon Towle and Clark Quinn

While recommender systems are in widespread use, they still experience problems. Many recommender systems produce recommendations which the customers find unsatisfactory. Further, these systems often suffer from problems when there are not enough participants, or when new products enter the system. We perceive an opportunity for knowledge-based recommender systems to gain leverage on recommendation tasks by using explicit models of both the user of the system and the products being recommeded. This differs from previous systems which, when they use a user model, have used one that is inferred from the ratings given by that user (i.e., an implicit user model). We believe that the additional information given by the user and product models can give the system leverage in difficult recommendation tasks, and also alleviate both the "early rater" problem and the "sparse ratings" problem experienced by current recommender systems~


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