Abstract
This chapter gives an overview of the area of explanations in recommender systems. We approach the literature from the angle of evaluation: that is, we are interested in what makes an explanation “good”, and suggest guidelines as how to best evaluate this. We identify seven benefits that explanations may contribute to a recommender system, and relate them to criteria used in evaluations of explanations in existing systems, and how these relate to evaluations with live recommender systems. We also discuss how explanations can be affected by how recommendations are presented, and the role the interaction with the recommender system plays w.r.t. explanations. Finally, we describe a number of explanation styles, and how they may be related to the underlying algorithms. Examples of explanations in existing systems are mentioned throughout.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pandora (2006). http://www.pandora.com
Movielens dataset (2009). http://www.grouplens.org/node/73
Netflix dataset (2009). http://www.netflixprize.com/
Newsmap (2009). http://www.marumushi.com/apps/newsmap/index.cfm
Nutking (2010). http://nutking.ectrldev.com/nutking/jsp/language. do?action=english
Adrissono, L., Goy, A., Petrone, G., Segnan, M., Torasso, P.: Intrigue: Personalized recommendation of tourist attractions for desktop and handheld devices. Applied Artificial Intelligence 17, 687–714 (2003)
Ahn, J.W., Brusilovsky, P., Grady, J., He, D., Syn, S.Y.: Open user profiles for adaptive news systems: help or harm? In: WWW ’07: Proceedings of the 16th international conference on World Wide Web, pp. 11–20. ACM Press, New York, NY, USA (2007).
Andersen, S.K., Olesen, K.G., Jensen, F.V.: HUGIN—a shell for building Bayesian belief universes for expert systems. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1990)
Bederson, B., Shneiderman, B., Wattenberg, M.: Ordered and quantum treemaps: Making effective use of 2d space to display hierarchies. ACM Transactions on Graphics 21(4), 833–854. (2002)
Bennett, S.W., Scott., A.C.: The Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, chap. 19 - Specialized Explanations for Dosage Selection, pp. 363–370. Addison-Wesley Publishing Company (1985)
Bilgic, M., Mooney, R.J.: Explaining recommendations: Satisfaction vs. promotion. In: Proceedings of the Wokshop Beyond Personalization, in conjunction with the International Conference on Intelligent User Interfaces, pp. 13–18 (2005)
Billsus, D., Pazzani, M.J.: A personal news agent that talks, learns, and explains. In: Proceedings of the Third International Conference on Autonomous Agents, pp. 268–275 (1999)
Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User- Adapted Interaction 12(4), 331–370 (2002)
Burke, R.D., Hammond, K.J., Young, B.C.: Knowledge-based navigation of complex information spaces. In: AAAI/IAAI, Vol. 1, pp. 462–468 (1996)
Carenini, G., Mittal, V., Moore, J.: Generating patient-specific interactive natural language explanations. Proc Annu Symp Comput Appl Med Care pp. 5–9 (1994)
Chen, L., Pu, P.: Trust building in recommender agents. In: WPRSIUI in conjunction with Intelligent User Interfaces, pp. 93–100 (2002)
Chen, L., Pu, P.: Hybrid critiquing-based recommender systems. In: Intelligent User Interfaces, pp. 22–31 (2007)
Cosley, D., Lam, S.K., Albert, I., Konstan, J.A., Riedl, J.: Is seeing believing?: how recommender system interfaces affect users’ opinions. In: CHI, Recommender systems and social computing, vol. 1, pp. 585–592 (2003).
Cramer, H., Evers, V., Someren, M.V., Ramlal, S., Rutledge, L., Stash, N., Aroyo, L., Wielinga, B.: The effects of transparency on perceived and actual competence of a contentbased recommender. In: Semantic Web User Interaction Workshop, CHI (2008)
Cramer, H.S.M., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., Wielinga, B.J.: The effects of transparency on trust in and acceptance of a content-based art recommender. User Model. User-Adapt. Interact 18(5), 455–496 (2008).
Czarkowski, M.: A scrutable adaptive hypertext. Ph.D. thesis, University of Sydney (2006)
Doyle, D., Tsymbal, A., Cunningham, P.: A review of explanation and explanation in casebased reasoning. Tech. rep., Department of Computer Science, Trinity College, Dublin (2003)
Felfernig, A., Gula, B.: Consumer behavior in the interaction with knowledge-based recommender applications. In: ECAI 2006Workshop on Recommender Systems, pp. 37–41 (2006)
Fogg, B., Marshall, J., Kameda, T., Solomon, J., Rangnekar, A., Boyd, J., Brown, B.: Web credibility research: A method for online experiments and early study results. In: CHI 2001, pp. 295–296 (2001)
Fogg, B.J., Soohoo, C., Danielson, D.R., Marable, L., Stanford, J., Tauber, E.R.: How do users evaluate the credibility of web sites?: a study with over 2,500 participants. In: Proceedings of DUX’03: Designing for User Experiences, no. 15 in Focusing on user-to-product relationships, pp. 1–15 (2003). URL http://doi.acm.org/10.1145/997078.997097
Ginty, L.M., Smyth, B.: Comparison-based recommendation. Lecture Notes in Computer Science 2416, 731–737 (2002).
Hance, E., Buchanan, B.: Rule-based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project. Addison-Wesley (1984)
Häubl, G., Trifts, V.: Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science 19, 4–21 (2000)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: ACM conference on Computer supported cooperative work, pp. 241–250 (2000)
Hingston, M.: User friendly recommender systems. Master’s thesis, Sydney University (2006)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM (2008)
Hunt, J.E., Price, C.J.: Explaining qualitative diagnosis. Engineering Applications of Artificial Intelligence 1(3), Pages 161–169 (1988)
Khan, O.Z., Poupart, P., Black, J.P.: Minimal sufficient explanations for mdps. In: Workshop on Explanation-Aware Computing associated with IJCAI (2009)
Krulwich, B.: The infofinder agent: Learning user interests through heuristic phrase extraction. IEEE Intelligent Systems 12, 22–27 (1997)
Lacave, C., Diéz, F.J.: A review of explanation methods for bayesian networks. The Knowledge Engineering Review 17:2, 107–127 (2002)
Lacave, C., Diéz, F.J.: A review of explanation methods for heuristic expert systems. The Knowledge Engineering Review 17:2, 107–127 (2004)
Lewis, C., Rieman, J.: Task-centered user interface design: a practical introduction. University of Colorado (1994)
Lopez-Suarez, A., Kamel, M.: Dykor: a method for generating the content of explanations in knowledge systems. Knowledge-based Systems 7(3), 177–188 (1994)
McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: Thinking positively - explanatory feedback for conversational recommender systems. In: Proceedings of the European Conference on Case-Based Reasoning (ECCBR-04) Explanation Workshop,, pp. 115–124 (2004)
McNee, S., Lam S.K.and Guetzlaff, C., Konstan J.A.and Riedl, J.: Confidence displays and training in recommender systems. In: INTERACT IFIP TC13 International Conference on Human-Computer Interaction, pp. 176–183 (2003)
McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. User Modeling pp. pp. 178–187 (2003)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: Extended Abstracts of the 2006 ACM Conference on Human Factors in Computing Systems (CHI 2006) (2006)
McSherry, D.: Explanation in recommender systems. Artificial Intelligence Review 24(2), 179 – 197 (2005)
Nielsen, J., Molich, R.: Heuristic evaluation of user interfaces. In: ACM CHI’90, pp. 249–256 (1990)
Ohanian, R.: Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising 19:3, 39–52 (1990)
O’Sullivan, D., Smyth, B., Wilson, D.C., McDonald, K., Smeaton, A.: Improving the quality of the personalized electronic program guide. User Modeling and User-Adapted Interaction 14, pp. 5–36 (2004)
Paramythis, A., Totter, A., Stephanidis, C.: A modular approach to the evaluation of adaptive user interfaces. In: S.Weibelzahl, D.N. Chin, G.Weber (eds.) Evaluation of Adaptive Systems in conjunction with UM’01, pp. 9–24 (2001)
Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13, 393–408 (1999)
Pu, P., Chen, L.: Trust building with explanation interfaces. In: IUI’06, Recommendations I, pp. 93–100 (2006).
Pu, P., Chen, L.: Trust-inspiring explanation interfaces for recommender systems. Knowledge-based Systems 20, 542–556 (2007)
Rafter, R., Smyth, B.: Conversational collaborative recommendation - an experimental analysis. Artif. Intell. Rev 24(3-4), 301–318 (2005). URL http://dx.doi.org/10.1007/s10462-005-9004-8
Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Dynamic critiquing. In: P. Funk, P.A. González-Calero (eds.) ECCBR, Lecture Notes in Computer Science, vol. 3155, pp. 763–777. Springer (2004)
Roth-Berghofer, T., Schulz, S., Leake, D.B., Bahls, D.:Workshop on explanation-aware computing. In: ECAI (2008)
Roth-Berghofer, T., Tintarev, N., Leake, D.B.: Workshop on explanation-aware computing. In: IJCAI (2009)
Sinha, R., Swearingen, K.: The role of transparency in recommender systems. In: Conference on Human Factors in Computing Systems, pp. 830–831 (2002)
Sørmo, F., Cassens, J., Aamodt, A.: Explanation in case-based reasoning perspectives and goals. Artificial Intelligence Review 24(2), 109 – 143 (2005)
Swearingen, K., Sinha, R.: Interaction design for recommender systems. In: Designing Interactive Systems, pp. 25–28 (2002).
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Justified recommendations based on content and rating data. In:WebKDDWorkshop onWeb Mining andWeb Usage Analysis (2008)
Thompson, C.A., G¨oker, M.H., Langley, P.: A personalized system for conversational recommendations. J. Artif. Intell. Res. (JAIR) 21, 393–428 (2004).
Tintarev, N., Masthoff, J.: Over- and underestimation in different product domains. In:Workshop on Recommender Systems associated with ECAI (2008)
Tintarev, N., Masthoff, J.: Personalizing movie explanations using commercial meta-data. In: Adaptive Hypermedia (2008)
Vig, J., Sen, S., Riedl, J.: Tagsplanations: Explaining recommendations using tags. In: Intelligent User Interfaces (2009)
Wang, W., Benbasat, I.: Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Managment Information Systems 23, 217–246 (2007)
Wärnestål, P.: Modeling a dialogue strategy for personalized movie recommendations. In: Beyond Personalization Workshop, pp. 77–82 (2005)
Wärnestål, P.: User evaluation of a conversational recommender system. In: Proceedings of the 4th Workshop on Knowledge and Reasoning in Practical Dialogue Systems, pp. 32–39 (2005)
Wick, M.R., Thompson, W.B.: Reconstructive expert system explanation. Artif. Intell. 54(1- 2), 33–70 (1992).
Ye, L., Johnson, P., Ye, L.R., Johnson, P.E.: The impact of explanation facilities on user acceptance of expert systems advice. MIS Quarterly 19(2), 157–172 (1995).
Yee, K.P., Swearingen, K., Li, K., Hearst, M.: Faceted metadata for image search and browsing. In: ACM Conference on Computer-Human Interaction (2003)
Zaslow, J.: Oh no! My TiVo thinks I’m gay (2002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Tintarev, N., Masthoff, J. (2011). Designing and Evaluating Explanations for Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-85820-3_15
Download citation
DOI: https://doi.org/10.1007/978-0-387-85820-3_15
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-85819-7
Online ISBN: 978-0-387-85820-3
eBook Packages: Computer ScienceComputer Science (R0)