Jonathan L. Herlocker
Joseph A. Konstan
John Riedl
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.
- 1.Buchanan,B.,Shortliffe,E.,(Eds.)1984.Rule-Based Expert Syst ms:The MYCIN Exp rim nts of the Stanford H uristic Programming Project .Addison-Wesley,Reading,MA. Google Scholar
Digital Library
- 2.Lauer,T.W.,Peacock E.,Graesser,A.C.,(Eds.)1985.Qu stions and Information Syst ms .Lawrence Erlbaum and Associates. Google ScholarDigital Library
- 3.Graesser,A.C.,Black,J.B.,(Eds.)1985.The Psychology of Qu stions .Lawrence Erlbaum and Associates.Google Scholar
- 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.Google Scholar
- 5.Herlocker,J.L.,Konstan,J.A.,Borchers,A.,Riedl,J.,1999.An algorithmic framework for performing collaborative filtering. Proceedings of the 1999 Conference on Research and Development in Information R trieval . Google ScholarDigital Library
- 6.Hill,W.,Stead,L.,Rosenstein,M.,Furnas,G.W.,1995. Recommending and Evaluating Choices in a Virtual Community of Use.Proceedings of ACM CHI'95 Conference on human factors in computing syst ms .Denver,CO.,(pp.194- 201). Google ScholarDigital Library
- 7.Horvitz,E.J.,Breese,J.S.,Henrion,M.,1988.Decision Theory in Expert Systems and Artificial Intelligence.International Journal of Approximate Reasoning 2 (3),247-302. Google ScholarDigital Library
- 8.Johnson,H.,Johnson,P.,1993.Explanation facilities and interactive systems.Proceedings of Intellig nt User Interfaces '93 .(pp.159-166). Google ScholarDigital Library
- 9.Konstan,J.A.,Miller,B.N.,Maltz,D.,Herlocker,J.L., Gordon,L.R.,Riedl,J.,1997.GroupLens:Applying collaborative filtering to Usenet news.Communications of the ACM 40 (3),77-87. Google ScholarDigital Library
- 10.Miller,C.A.,Larson,R.,1992.An Explanatory and ``Argumentative''Interface for a Model-Based Diagnostic System.Proceedings of User Interface Software and Technology (UIST '92).(pp.43-52). Google ScholarDigital Library
- 11.Norman,D.A.1989.The D sign of Ev ryday Things .Currency- Doubleday,New York.Google Scholar
- 12.Resnick,P.,Iacovou,N.,Suchak,M.,Bergstrom,P.,Riedl,J., 1994.GroupLens:An open architecture for collaborative filtering of netnews.Proceedings of 1994 Confer nc on Computer Supported Collaborativ Work .(pp.175-186). Google ScholarDigital Library
- 13.Shardanand,U.,Maes,P.,1995.Social Information Filtering: Algorithms for Automating "Word of Mouth".Proceedings of ACM CHI '95 .Denver,CO.,(pp.210-217). Google ScholarDigital Library
- 14.Toulmin S.E.1958.The Uses of Argument .Cambridge University PressGoogle Scholar
Index Terms
- Explaining collaborative filtering recommendations
Recommendations
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsUser-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Integrating collaborative filtering and matching-based search for product recommendations
Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good ...
Explaining neighborhood-based recommendations
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrievalRecommender Systems (RS) attempt to discover users' preferences, and to learn about them in order to anticipate their needs. The main task normally associated with a RS is to offer suggestions for items. However, for most users, RSs are black boxes, ...
Comments