skip to main content
research-article

The Tag Genome: Encoding Community Knowledge to Support Novel Interaction

Published:01 September 2012Publication History
Skip Abstract Section

Abstract

This 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 item in an information space based on its relationship to a common set of tags. We present a machine learning approach for computing the tag genome, and we evaluate several learning models on a ground truth dataset provided by users. We describe an application of the tag genome called Movie Tuner which enables users to navigate from one item to nearby items along dimensions represented by tags. We present the results of a 7-week field trial of 2,531 users of Movie Tuner and a survey evaluating users’ subjective experience. Finally, we outline the broader space of applications of the tag genome.

Skip Supplemental Material Section

Supplemental Material

References

  1. Bateman, S., Gutwin, C., and Nacenta, M. 2008. Seeing things in the clouds: The effect of visual features on tag cloud selections. In Proceedings of the 19th ACM Conference on Hypertext and Hypermedia (Hypertext’08). ACM, New York, NY, 193--202. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bates, D. and Sarkar, D. 2007. Linear mixed-effects models using S4 classes. http://CRAN.R-project.org.Google ScholarGoogle Scholar
  3. Bird, S., Loper, E., and Klein, E. 2009. Natural Language Processing with Python. O’Reilly Media Inc. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Burke, R. 2002. Hybrid recommender systems: Survey and experiments. User Model. User-Adapted Interac. 12, 4, 331--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Burke, R. D., Hammond, K. J., and Young, B. C. 1996. Knowledge-based navigation of complex information spaces. In Proceedings of the 13th National Conference on Artificial Intelligence (AAAI’96). AAAI Press, 462--468. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Burke, R. D., Hammond, K. J., and Young, B. C. 1997. The FindMe approach to assisted browsing. IEEE Expert 12, 32--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Chen, L. and Pu, P. 2006. Evaluating critiquing-based recommender agents. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI’06). Vol. 1, 157--162. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, R. A. 1990. Indexing by latent semantic analysis. J. Amer. Soc. Info. Sci. 41, 6, 391--407.Google ScholarGoogle ScholarCross RefCross Ref
  9. Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., and Vapnik, V. 1997. Support vector regression machines. In Advances in Neural Information Processing Systems, Vol. 9, 155--161.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Eck, D., Lamere, P., Bertin-Mahieux, T., and Green, S. 2007. Automatic generation of social tags for music recommendation. In Advances in Neural Information Processing Systems, Vol. 20.Google ScholarGoogle Scholar
  11. Faltings, B., Pu, P., Torrens, M., and Viappiani, P. 2004. Designing example-critiquing interaction. In Proceedings of the 9th International Conference on Intelligent User Interfaces (IUI’04). ACM, New York, NY, 22--29. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Feinstein, D. and Smadja, F. 2006. Hierarchical tags and faceted search: The RawSugar approach. In SIGIR Conference on Research and Development in Information Retrieval Workshop on Faceted Search. 23--25.Google ScholarGoogle Scholar
  13. Fletcher, R. 1981. Practical Methods of Optimization: Vol. 2: Constrained Optimization. John Wiley and Sons.Google ScholarGoogle Scholar
  14. Gelman, A. and Hill, J. 2007. Data Analysis Using Regression and Multilevel Hierarchical Models. Cambridge University Press, Cambridge, UK.Google ScholarGoogle Scholar
  15. Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. 2003. Bayesian Data Analysis, 2nd Ed.. Chapman & Hall/CRC.Google ScholarGoogle Scholar
  16. Golder, S. A. and Huberman, B. A. 2006. Usage patterns of collaborative tagging systems. J. Info. Sci. 32, 198--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Green, S. J., Lamere, P., Alexander, J., Maillet, F., Kirk, S., Holt, J., Bourque, J., and Mak, X. 2009. Generating transparent, steerable recommendations from textual descriptions of items. In Proceedings of the ACM Conference on Recommender Systems (RecSys’09). ACM, New York, NY, 281--284. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Guan, Z., Wang, C., Bu, J., Chen, C., Yang, K., Cai, D., and He, X. 2010. Document recommendation in social tagging services. In Proceedings of the 19th International Conference on World Wide Web (WWW’10). ACM, New York, NY, 391--400. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Guyon, I. and Elisseeff, A. 2003. An introduction to variable and feature selection. J. Machine Learn. Res. 3, 1157--1182. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Halvey, M. J. and Keane, M. T. 2007. An assessment of tag presentation techniques. In Proceedings of the 16th International Conference on the World Wide Web (WWW’07). ACM, New York, NY, 1313--1314. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Heymann, P., Ramage, D., and Garcia-Molina, H. 2008. Social tag prediction. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’08). ACM, New York, NY, 531--538. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Hingston, M. and Kay, J. 2006. User friendly recommender systems (honors thesis).Google ScholarGoogle Scholar
  23. Jedetski, J., Adelman, L., and Yeo, C. 2002. How web site decision technology affects consumers. IEEE Internet Comput. 6, 72--79. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jäschke, R., Marinho, L. B., Hotho, A., Schmidt-Thieme, L., and Stumme, G. 2007. Tag recommendations in folksonomies. In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’07). Vol. 4702, Springer, 506--514. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Kammerer, Y., Nairn, R., Pirolli, P., and Chi, H. 2009. Signpost from the masses: Learning effects in an exploratory social tag search browser. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’09). 625--634. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Kelley, C. T. 1999. Iterative methods for optimization. In SIAM Frontiers in Applied Mathematics, 18.Google ScholarGoogle Scholar
  27. Kohavi, R. and John, G. H. 1997. Wrappers for feature subset selection. Art. Intell. 97, 1--2, 273--324. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Kulesza, T., Wong, W.-K., Stumpf, S., Perona, S., White, R., Burnett, M. M., Oberst, I., and Ko, A. J. 2009. Fixing the program my computer learned: Barriers for end users, challenges for the machine. In Proceedings of the 14th International Conference on Intelligent User Interfaces (IUI’09). ACM, New York, NY, 187--196. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Linden, G., Hanks, S., and Lesh, N. 1997. Interactive assessment of user preference models: The automated travel assistant. In Proceedings of User Modeling’97. Springer, 67--78.Google ScholarGoogle Scholar
  30. Manning, C. D., Raghavan, P., and Schütze, H. 2008. Introduction to Information Retrieval. Cambridge University Press, Cambridge, UK. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Marlow, C., Naaman, M., Boyd, D., and Davis, M. 2006. HT06, tagging paper, taxonomy, flickr, academic article, to read. In Proceedings of the 17th Conference on Hypertext and Hypermedia (Hypertext’06). ACM, New York, NY, 31--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. McCallum, A. and Nigam, K. 1998. A comparison of event models for naive bayes text classification. In Proceedings of the Conference on Artificial Intelligence Workshop on Learning for Text Categorization. AAAI Press, 41--48.Google ScholarGoogle Scholar
  33. McCarthy, K., Reilly, J., McGinty, L., and Smyth, B. 2005. Experiments in dynamic critiquing. In Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI’05). ACM, New York, NY, 175--182. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. McCullagh, P. and Nelder, J. 1989. Generalized Linear Models 2nd Ed. Chapman & Hall/CRC.Google ScholarGoogle Scholar
  35. Mooney, R. J. and Roy, L. 2000. Content-based book recommending using learning for text categorization. In Proceedings of the 5th ACM Conference on Digital Libraries. ACM, New York, NY, 195--204. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Možina, M., Demšar, J., Kattan, M., and Zupan, B. 2004. Nomograms for visualization of naive bayesian classifier. In Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD’04). Springer-Verlag Berlin, 337--348. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Payne, J. W., Bettman, J., and Johnson, E. J. 1993. The Adaptive Decision Maker. Cambridge University Press. Cambridge, UK.Google ScholarGoogle Scholar
  38. Porter, M. 1980. An algorithm for suffix stripping. Program: Elect. Library Info. Syst. 14, 3, 130--137.Google ScholarGoogle ScholarCross RefCross Ref
  39. Poulin, B., Eisner, R., Szafron, D., Lu, P., Greiner, R., Wishart, D. S., Fyshe, A., Pearcy, B., MacDonell, C., and Anvik, J. 2006. Visual explanation of evidence in additive classifiers. In Proceedings of the 18th Conference on Innovative Applications of Artificial Intelligence (AAAI’06). Vol. 2, AAAI Press, 1822--1829. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Pu, P. and Chen, L. 2005. Integrating tradeoff support in product search tools for e-commerce sites. In Proceedings of the 6th ACM Conference on Electronic Commerce (EC’05). ACM, New York, NY, 269--278. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. R Development Core Team. 2010. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.Google ScholarGoogle Scholar
  42. Raudenbush, S. and Bryk, A. 2002. Hierarchical Linear Models 2nd Ed. Sage Publications, Thousand Oaks, CA.Google ScholarGoogle Scholar
  43. Rivadeneira, A. W., Gruen, D. M., Muller, M. J., and Millen, D. R. 2007. Getting our head in the clouds: Toward evaluation studies of tagclouds. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’07). ACM, New York, NY, 995--998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Salton, G. and McGill, M. 1983. Introduction to Modern Information Retrival. McGraw-Hill. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Salton, G., Wong, A., and Yang, C. S. 1975. A vector space model for automatic indexing. Comm. ACM 18, 613--620. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Sen, S., Lam, S. K., Rashid, A. M., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F. M., and Riedl, J. 2006. tagging, communities, vocabulary, evolution. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW’06). ACM, New York, NY, 181--190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Sen, S., Harper, F. M., LaPitz, A., and Riedl, J. 2007. The quest for quality tags. In Proceedings of the International ACM Conference on Supporting Group Work (GROUP’07). ACM, New York, NY, 361--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Sen, S., Vig, J., and Riedl, J. 2009. Tagommenders: Connecting users to items through tags. In Proceedings of the 18th International Converence on Word Wide Web (WWW’09). ACM, New York, NY, 671--680. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Shneiderman, B. 1994. Dynamic queries for visual information seeking. IEEE Softw. 11, 70--77. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Sigurbjörnsson, B. and van Zwol, R. 2008. Flickr tag recommendation based on collective knowledge. In Proceeding of the 17th International Conference on the World Wide Web (WWW’08). ACM, New York, NY, 327--336. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Smith, D. C., Irby, C., Kimball, R., Berplank, B., and Harslem, E. 1990. Designing the Star user interface. In Human-Computer Interaction, 237--259. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Smyth, B., McGinty, L., Reilly, J., and McCarthy, K. 2004. Compound critiques for conversational recommender systems. In Proceedings of (Web Intelligence’04). IEEE Computer Society, Los Alamitos, CA, 145--151. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Sparling, E. I. and Sen, S. 2011. Rating: How difficult is it? In Proceedings of the 2011 ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Symeonidis, P., Nanopoulos, A., and Manolopoulos, Y. 2008. Tag recommendations based on tensor dimensionality reduction. In Proceedings of the ACM Conference on Recommender Systems (RecSys’08). ACM, New York, NY, 43--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Teevan, J., Dumais, S., and Gutt, Z. 2008. Challenges for supporting faceted search in large, heterogeneous corpora like the web. In Proceedings of the Second Workshop on Human-Computer Interaction and Information Retrieval (HCIR’08).Google ScholarGoogle Scholar
  56. Tintarev, N. and Masthoff, J. 2007. Effective explanations of recommendations: User-centered design. In Proceedings of the ACM Conference on Recommender Systems (RecSys’07). ACM, New York, NY, 153--156. Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Tso-Sutter, K. H. L., Marinho, L. B., and Schmidt-Thieme, L. 2008. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the ACM Symposium on Applied Computing (SAC’08). ACM, New York, NY, 1995--1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Ulges, A., Schulze, C., Keysers, D., and Breuel, T. M. 2008. A system that learns to tag videos by watching YouTube. In Proceedings of the 6th International Conference on Computer Vision Systems (ICVS’08). Springer-Verlag, Berlin, 415--424. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Vig, J., Sen, S., and Riedl, J. 2009. Tagsplanations: Explaining recommendations using tags. In Proceedings of the 13th International Conference on Intelligent User Interfaces (IUI’09). ACM, New York, NY, 47--56. Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Vig, J., Sen, S., and Riedl, J. 2011. Navigating the tag genome. In Proceedings of the 15th International Conference on Intelligent User Interfaces (IUI’11). ACM, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Wu, L., Yang, L., Yu, N., and Hua, X.-S. 2009. Learning to tag. In Proceedings of the 18th International Conference on the World Wide Web (WWW’09). ACM, New York, NY, 361--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. Yee, K.-P., Swearingen, K., Li, K., and Hearst, M. 2003. Faceted metadata for image search and browsing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’03). ACM, New York, NY, 401--408. Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Zhai, C. and Lafferty, J. 2001. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’01). ACM, New York, NY, 334--342. Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. Zhang, J. and Pu, P. 2006. A comparative study of compound critique generation in conversational recommender systems. In Proceedings of Adaptive Hypermedia’06. Springer, 234--243. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. The Tag Genome: Encoding Community Knowledge to Support Novel Interaction

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Interactive Intelligent Systems
          ACM Transactions on Interactive Intelligent Systems  Volume 2, Issue 3
          Special Issue on Common Sense for Interactive Systems
          September 2012
          171 pages
          ISSN:2160-6455
          EISSN:2160-6463
          DOI:10.1145/2362394
          Issue’s Table of Contents

          Copyright © 2012 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 1 September 2012
          • Accepted: 1 April 2012
          • Revised: 1 March 2012
          • Received: 1 October 2011
          Published in tiis Volume 2, Issue 3

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader