User Evaluation of Fusion-based Recommender Systems for Serendipity-oriented Recommendation

In recent years, studies have focused on the development of recommender systems that consider measures that go beyond simply the accuracy of the system. One such measure, serendipity, is de ned as a measure that indicates how the recommender system can nd unexpected and useful items for users. We have previously proposed a fusion-based recommender system as a serendipity-oriented recommender system. In this study, we improve upon this system by considering the concept of serendipity. Our system possesses mechanisms that can cause extrinsic and intrinsic accidents, and it enables users to derive some value from such accidents through their sagacity. We consider that such mechanisms are required for the development of the serendipityoriented recommender system. The key idea of this system is the fusion-based approach, through which the system mixes two user-input items to nd new items that have the mixed features. The contributions of this paper are as follows: providing an improved fusion-based recommender system that adopts a fusion-based approach to improve serendipity; practically evaluating the recommender system through user tests using a real book data set from Rakuten Books; and showing the e ectiveness of the system compared to recommender systems on websites such as Amazon from the viewpoint of serendipity.