Investigating the Relationship Between User Preferences, Previous Ratings and User Judgments Related to Serendipity

Recent research suggests that users of a recommender system may like to receive useful unexpected suggestions that provide a pleasant surprise. This concept, called serendipity, is one of the aspects that have been proposed to meet user expectations for the recommendations they receive. Introducing serendipity means going beyond the “more of the same” aspect that past recommender systems are criticized for. A new approach has recently been proposed to create user models from their previous ratings. In this paper, we show how this user modelling approach can be used to investigate the relationship between users’ preferences, their previous ratings and their judgments related to serendipity. Experiments in the movie domain show that the more relevant an item is to a user, the more willing the user is to discover attributes that are unfamiliar to him, as long as these attributes do not play an important role in his ratings.