Jesse Vig
John Riedl
ABSTRACT
Tags help users understand a rich information space, by showing them specific text annotations for each item in the space and enabling them to search by these annotations. Often, however, users may wish to move from one item to other items that are similar overall, but that differ in key characteristics. For example, a user who loves Pulp Fiction might want to see a similar movie, but might be in a mood for a less "dark" movie. This paper introduces Movie Tuner, a novel interface that supports navigation from one item to nearby items along dimensions represented by tags. Movie Tuner is based on a data structure called the tag genome, which is described in separate work. The tag genome encodes each item's relationship to a common set of tags by applying machine learning algorithms to user-contributed content. The present paper discusses our design of Movie Tuner, including algorithms for navigating to new items and for suggesting tags for navigation. We present the results of a 7-week field trial of 2,531 users of Movie Tuner, and of a survey evaluating users' subjective experience.
Supplemental Material
- R. D. Burke, K. J. Hammond, and B. C. Young. The FindMe approach to assisted browsing. IEEE Expert, 12:32--40, 1997. Google Scholar
Digital Library
- L. Chen and P. Pu. Evaluating critiquing-based recommender agents. In In Proc. AAAI 2006, pages 157--162, 2006. Google ScholarDigital Library
- B. Faltings, P. Pu, M. Torrens, and P. Viappiani. Designing example-critiquing interaction. In IUI '04, pages 22--29, New York, NY, USA, 2004. ACM. Google ScholarDigital Library
- R. Fletcher. Practical Methods of Optimization: Vol. 2: Constrained Optimization. John Wiley and Sons, 1981.Google Scholar
- S. J. Green, P. Lamere, J. Alexander, F. Maillet, S. Kirk, J. Holt, J. Bourque, and X.-W. Mak. Generating transparent, steerable recommendations from textual descriptions of items. In RecSys '09, pages 281--284, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- Y. Kammerer, R. Nairn, P. Pirolli, and H. Chi. Signpost from the masses: Learning effects in an exploratory social tag search browser. In CHI '09, pages 625--634. Google ScholarDigital Library
- G. Linden, S. Hanks, and N. Lesh. Interactive assessment of user preference models: The automated travel assistant. In User Modeling '97, pages 67--78. Springer, 1997.Google ScholarCross Ref
- K. McCarthy, J. Reilly, L. McGinty, and B. Smyth. Experiments in dynamic critiquing. In IUI '05, pages 175--182, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- S. Niwa, T. Doi, and S. Honiden. Web page recommender system based on folksonomy mining. In ITNG '06, pages 388--393, Washington, DC, USA, 2006. IEEE Computer Society. Google ScholarDigital Library
- J. W. Payne, J. Bettman, and E. J. Johnson. The Adaptive Decision Maker. Cambridge University Press, 1993.Google ScholarCross Ref
- G. Salton and M. McGill. Introduction to Modern Information Retrival. McGraw-Hill, 1983. Google ScholarDigital Library
- S. Sen, F. M. Harper, A. LaPitz, and J. Riedl. The quest for quality tags. In GROUP '07, pages 361--370, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- S. Sen, S. K. Lam, A. M. Rashid, D. Cosley, D. Frankowski, J. Osterhouse, F. M. Harper, and J. Riedl. tagging, communities, vocabulary, evolution. In CSCW '06, pages 181--190, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
- S. Sen, J. Vig, and J. Riedl. Tagommenders: Connecting users to items through tags. In WWW '09, pages 671--680, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- C. Shirky. Ontology is overrated. http://www.shirky.com/writings/ontology_overrated.html, 2005. Retrieved on May 26, 2007.Google Scholar
- D. C. Smith, C. Irby, R. Kimball, B. Berplank, and E. Harslem. Designing the Star user interface. Human-computer interaction, pages 237--259, 1990. Google ScholarDigital Library
- B. Smyth, L. McGinty, J. Reilly, and K. McCarthy. Compound critiques for conversational recommender systems. In Web Intelligence '04, pages 145--151, Washington, DC, USA, 2004. IEEE Computer Society. Google ScholarDigital Library
- J. Vig, S. Sen, and J. Riedl. Computing the tag genome. Technical report, University of Minnesota, 2010. http://www.grouplens.org/system/files/genome.pdf.Google Scholar
- J. Zhang and P. Pu. A comparative study of compound critique generation in conversational recommender systems. In Adaptive Hypermedia '06, pages 234--243. Springer, 2006. Google ScholarDigital Library
Index Terms
- Navigating the tag genome
Recommendations
The Tag Genome: Encoding Community Knowledge to Support Novel Interaction
Special Issue on Common Sense for Interactive SystemsThis article introduces the tag genome, a data structure that extends the traditional tagging model to provide enhanced forms of user interaction. Just as a biological genome encodes an organism based on a sequence of genes, the tag genome encodes an ...
Tagommenders: connecting users to items through tags
WWW '09: Proceedings of the 18th international conference on World wide webTagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders ...
Improving recommendation accuracy based on item-specific tag preferences
Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in contextIn recent years, different proposals have been made to exploit Social Web tagging information to build more effective recommender systems. The tagging data, for example, were used to identify similar users or were viewed as additional information about ...
Comments