On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

50 Pages Posted: 24 Jun 2013

See all articles by Panagiotis Adamopoulos

Panagiotis Adamopoulos

Emory University - Information Systems and Operations Management

Alexander Tuzhilin

New York University (NYU) - Leonard N. Stern School of Business; New York University (NYU) - Department of Information, Operations, and Management Sciences

Date Written: June 2013

Abstract

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this paper, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they expect from the system. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. Besides, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists.We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing the users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” data sets to compare our recommendation results with some other standard baseline methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage and aggregate diversity, while avoiding any accuracy loss.

Keywords: Algorithms, Design, Experimentation, Human Factors, Measurement, Performance, Evaluation, Novelty, Recommendations, Recommender Systems, Serendipity, Unexpectedness, Utility Theory

Suggested Citation

Adamopoulos, Panagiotis and Tuzhilin, Alexander, On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected (June 2013). NYU Working Paper No. 2451/31832. Available at SSRN: https://ssrn.com/abstract=2282999

Panagiotis Adamopoulos

Emory University - Information Systems and Operations Management ( email )

1300 Clifton Road
Atlanta, GA 30322
United States

Alexander Tuzhilin

New York University (NYU) - Leonard N. Stern School of Business ( email )

44 West 4th Street
Suite 9-160
New York, NY NY 10012
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

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