Tagsplanations: explaining recommendations using tags

ABSTRACT

While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.

REFERENCES

• 1: M. Bilgic and R. J. Mooney. Explaining recommendations: Satisfaction vs. promotion. In Proceedings of Beyond Personalization Workshop, IUI, 2005.
• 2: Daniel Billsus , Michael J. Pazzani, A personal news agent that talks, learns and explains, Proceedings of the third annual conference on Autonomous Agents, p.268-275, April 1999, Seattle, Washington, United States [doi>10.1145/301136.301208]
• 3: Dan Cosley , Shyong K. Lam , Istvan Albert , Joseph A. Konstan , John Riedl, Is seeing believing?: how recommender system interfaces affect users' opinions, Proceedings of the SIGCHI conference on Human factors in computing systems, April 05-10, 2003, Ft. Lauderdale, Florida, USA [doi>10.1145/642611.642713]
• 4: J. Ellenberg. The psychologist might outsmart the math brains competing for the netflix prize. Wired Magazine, March 2008.
• 5: Scott A. Golder , Bernardo A. Huberman, Usage patterns of collaborative tagging systems, Journal of Information Science, v.32 n.2, p.198-208, April 2006 [doi>10.1177/0165551506062337]
• 6: Jonathan L. Herlocker , Joseph A. Konstan , John Riedl, Explaining collaborative filtering recommendations, Proceedings of the 2000 ACM conference on Computer supported cooperative work, p.241-250, December 2000, Philadelphia, Pennsylvania, United States [doi>10.1145/358916.358995]
• 7: Raymond J. Mooney , Loriene Roy, Content-based book recommending using learning for text categorization, Proceedings of the fifth ACM conference on Digital libraries, p.195-204, June 02-07, 2000, San Antonio, Texas, United States [doi>10.1145/336597.336662]
• 8: Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, p.285-295, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372071]
• 9: B. M. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Application of dimensionality reduction in recommender systems -- a case study. In ACM WebKDD 00 (Web-mining for ECommerce Workshop), New York, NY, USA, 2000. ACM.
• 10: Shilad Sen , F. Maxwell Harper , Adam LaPitz , John Riedl, The quest for quality tags, Proceedings of the 2007 international ACM conference on Supporting group work, November 04-07, 2007, Sanibel Island, Florida, USA [doi>10.1145/1316624.1316678]
• 11: Shilad Sen , Shyong K. Lam , Al Mamunur Rashid , Dan Cosley , Dan Frankowski , Jeremy Osterhouse , F. Maxwell Harper , John Riedl, tagging, communities, vocabulary, evolution, Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, November 04-08, 2006, Banff, Alberta, Canada [doi>10.1145/1180875.1180904]
• 12: C. Shirky. Ontology is overrated. http://www.shirky.com/writings/ontology overrated.html, 2005. Retrieved on May 26, 2007.
• 13: Rashmi Sinha , Kirsten Swearingen, The role of transparency in recommender systems, CHI '02 extended abstracts on Human factors in computing systems, April 20-25, 2002, Minneapolis, Minnesota, USA [doi>10.1145/506443.506619]
• 14: Nava Tintarev, Explanations of recommendations, Proceedings of the 2007 ACM conference on Recommender systems, October 19-20, 2007, Minneapolis, MN, USA [doi>10.1145/1297231.1297275]
• 15: Nava Tintarev , Judith Masthoff, Effective explanations of recommendations: user-centered design, Proceedings of the 2007 ACM conference on Recommender systems, October 19-20, 2007, Minneapolis, MN, USA [doi>10.1145/1297231.1297259]
• 16: N. Tintarev and J. Masthoff. A survey of explanations in recommender systems. In IEEE 23rd International Conference on Data Engineering Workshop, pages 801--810, 2007.

INDEX TERMS

Primary 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, Experimentation, Human Factors

Keywords:
explanations, recommender systems, tagging