How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation

Abstract

Recommendation serendipity is being increasingly recognized as being equally important as the other beyond-accuracy objectives (such as novelty and diversity), in eliminating the “filter bubble” phenomenon of the traditional recommender systems. However, little work has empirically verified the effects of serendipity on increasing user satisfaction and behavioral intention. In this paper, we report the results of a large-scale user survey (involving over 3,000 users) conducted in an industrial mobile e-commerce setting. The study has identified the significant causal relationships from novelty, unexpectedness, relevance, and timeliness to serendipity, and from serendipity to user satisfaction and purchase intention. Moreover, our findings reveal that user curiosity plays a moderating role in strengthening the relationships from novelty to serendipity and from serendipity to satisfaction. Our third contribution lies in the comparison of several recommender algorithms, which demonstrates the significant improvements of the serendipity-oriented algorithm over the relevance- and novelty-oriented approaches in terms of user perceptions. We finally discuss the implications of this experiment, which include the feasibility of developing a more precise metric for measuring recommendation serendipity, and the potential benefit of a curiosity-based personalized serendipity strategy for recommender systems.