skip to main content
10.1145/2043932.2044019acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
tutorial

Workshop on novelty and diversity in recommender systems - DiveRS 2011

Authors Info & Claims
Published:23 October 2011Publication History

ABSTRACT

Novelty and diversity have been identified as key dimensions of recommendation utility in real scenarios, and a fundamental research direction to keep making progress in the field. Yet recommendation novelty and diversity remain a largely open area for research. The DiveRS workshop gathered researchers and practitioners interested in the role of these dimensions in recommender systems. The workshop seeks to advance towards a better understanding of what novelty and diversity are, how they can improve the effectiveness of recommendation methods and the utility of their outputs. The workshop pursued the identification of open problems, relevant research directions, and opportunities for innovation in the recommendation business.

References

  1. Adomavicius, G. and Kwon, Y. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. IEEE Transactions on Knowledge and Data Engineering. In Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Agrawal, R., Gollapudi, S., Halverson, A., and Ieong, S. Diversifying search results. 2nd ACM International Conference on Web Search and Data Mining (WSDM 2009), Barcelona, Spain, 5--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Celma, O. and Herrera, P. A New Approach to Evaluating Novel Recommendations. 5th ACM International Conference on Recommender Systems (RecSys 2008), Lausanne, Switzerland, 179--186. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Clarke, C. L. A., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., and MacKinnon, I. Novelty and diversity in information retrieval evaluation. 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), Singapore, 659--666. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Fleder, D. M. and Hosanagar, K. Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management Science 35, 5, 2009, 697--712. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Lathia, N., Hailes, S., Capra, L., and Amatriain, X. Temporal Diversity in Recommender Systems. 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2010), Geneva, Switzerland, 210--217. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. McNee, S. M., Riedl, J., and Konstan, J. A. Being Accurate is Not Enough: How Accuracy Metrics have hurt Recommender Systems. ACM Conference on Human Factors in Computing Systems (CHI 2006),Extended Abstracts, Montréal, Canada, 1097--1101. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Vargas, S. and Castells, P. Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. 8th ACM International Conference on Recommender Systems (RecSys 2011), Chicago, IL, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Wang, J. and Zhu, J. Portfolio theory of information retrieval. 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2009), Boston, MA, USA, 115--122. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Zhang, M. and Hurley, N. Avoiding Monotony: Improving the Diversity of Recommendation Lists. 5th ACM International Conference on Recommender Systems (RecSys 2008), Lausanne, Switzerland, 123--130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Zhou, T., Kuscsik, Z., Liu, J-G., Medo, M., Wakeling, J. R., and Zhang, Y-C. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences of the United States of America 107, 10, 2010, 4511--4515.Google ScholarGoogle ScholarCross RefCross Ref
  12. Ziegler, C-N., McNee, S. M., Konstan, J. A., and Lausen, G. Improving recommendation lists through topic diversification. 14th International World Wide Web Conference (WWW 2005), Chiba, Japan, 22--32. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Workshop on novelty and diversity in recommender systems - DiveRS 2011

      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

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader