Fusion-based Recommender System for Serendipity-Oriented Recommendations

Abstract

In recent years, studies have focused on the development of recommender systems that emphasize criteria other than accuracy. One such measure, serendipity, is defined as a measure that indicates how the recommender system can find unexpected and useful items for users. In this study,we propose a fusion-based recommender system as a serendipity-oriented recommender system. Our system possesses mechanisms that can cause extrinsic and intrinsic cues, and it enables users to discover valuable items from such cues through their sagacity.We consider that such mechanisms are required for the development of the serendipity-oriented recommender system. The key idea of this system is the fusion-based approach, through which the system mixes two user-input items to find new items that have the mixed features. The contributions of this paper are as follows: providing a 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 effectiveness of the system compared to Amazon that is one of the recommender systems on websites from the viewpoint of serendipity.