The Impacts of Item Features and User Characteristics on Users' Perceived Serendipity of Recommendations

Abstract

Serendipity-oriented recommender systems have increasingly been recognized as useful to overcome the "filter bubble" problem of accuracy-oriented recommenders, by recommending unexpected and relevant items to users. However, most of existing systems are based on researchers' assumptions about the effect of item features on serendipity, but less from users' perspective to study what item features and even user characteristics might affect their perceived serendipity. In this paper, we have attempted to fill in this vacancy based on results of a large-scale user survey (involving over 10,000 users). We have analyzed the correlation between different types of features (i.e., numerical and categorical) with user perceptions, and furthermore identified the interaction effect from user characteristics (such as personality traits and curiosity). We finally discuss the implications of our work to augment the effectiveness of current serendipity-oriented recommender systems.