Explaining collaborative filtering recommendations

ABSTRACT

Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users.

REFERENCES

• 1: Bruce G. Buchanan , Edward H. Shortliffe, Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence), Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1984
• 2: Thomas W. Lauer , Arthur C. Graesser, Questions and information systems, Lawrence Erlbaum Associates, Inc., Mahwah, NJ, 1992
• 3: Graesser,A.C.,Black,J.B.,(Eds.)1985.The Psychology of Qu stions .Lawrence Erlbaum and Associates.
• 4: Dahlen,B.J.,Konstan,J.A.,Herlocker,J.L.,Good,N.,Borchers,A., Riedl,J.,1998.Jump-starting movielens:User benefits of starting a collaborative filtering system with "dead data". University of Minnesota TR 98-017.
• 5: Jonathan L. Herlocker , Joseph A. Konstan , Al Borchers , John Riedl, An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.230-237, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312682]
• 6: Will Hill , Larry Stead , Mark Rosenstein , George Furnas, Recommending and evaluating choices in a virtual community of use, Proceedings of the SIGCHI conference on Human factors in computing systems, p.194-201, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223929]
• 7: Eric J. Horvitz , John S. Breese , Max Henrion, Decision theory in expert systems and artificial intelligence, International Journal of Approximate Reasoning, v.2 n.3, p.247-302, July 1988 [doi>10.1016/0888-613X(88)90120-X]
• 8: Hilary Johnson , Peter Johnson, Explanation facilities and interactive systems, Proceedings of the 1st international conference on Intelligent user interfaces, p.159-166, January 04-07, 1993, Orlando, Florida, United States [doi>10.1145/169891.169951]
• 9: Joseph A. Konstan , Bradley N. Miller , David Maltz , Jonathan L. Herlocker , Lee R. Gordon , John Riedl, GroupLens: applying collaborative filtering to Usenet news, Communications of the ACM, v.40 n.3, p.77-87, March 1997 [doi>10.1145/245108.245126]
• 10: Christopher A. Miller , Raymond Larson, An explanatory and “argumentative” interface for a model-based diagnostic system, Proceedings of the 5th annual ACM symposium on User interface software and technology, p.43-52, November 15-18, 1992, Monteray, California, United States [doi>10.1145/142621.142627]
• 11: Norman,D.A.1989.The D sign of Ev ryday Things .Currency- Doubleday,New York.
• 12: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]
• 13: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]
• 14: Toulmin S.E.1958.The Uses of Argument .Cambridge University Press

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.3 Group and Organization Interfaces
          Subjects: Collaborative computing

General Terms:
Design, Human Factors, Management, Measurement, Performance, Reliability, Theory

Keywords:
GroupLens, MoviesLens, collaborative filtering, explanations, recommender systems