The keepup recommender system

ABSTRACT

In this short paper, we describe our RSS recommender system, KeepUP. Too often recommender systems are seen as black box systems, resulting in general perplexity and dissatisfaction from users who are treated as passive, isolated consumers. Recent literature observes that recommendations rarely occur within such isolation and that there may be potential within more socially-orientated approaches. With KeepUP, we outline the design of a recommendation process that is based on an implicit social network where the relevancy and meaning of information can be negotiated not only with the recommender system but also with other users. Our overall goal is to support the formation and development of online communities of interest.

REFERENCES

• 1: Aïmeur, E. and Mani-Onana, F. S. Better control on recommender systems. IEEE Joint Conference on E-Commerce Technology (CEC'06), 2006, 297--306.
• 2: Charu C. Aggarwal , Joel L. Wolf , Kun-Lung Wu , Philip S. Yu, Horting hatches an egg: a new graph-theoretic approach to collaborative filtering, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.201-212, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312230]
• 3: Robin D. Burke , Kristian J. Hammond , Benjamin C. Young, The FindMe Approach to Assisted Browsing, IEEE Expert: Intelligent Systems and Their Applications, v.12 n.4, p.32-40, July 1997 [doi>10.1109/64.608186 ]
• 4: 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]
• 5: Zan Huang , Hsinchun Chen , Daniel Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.116-142, January 2004 [doi>10.1145/963770.963775]
• 6: Saverio Perugini , Marcos André Gonçalves , Edward A. Fox, Recommender Systems Research: A Connection-Centric Survey, Journal of Intelligent Information Systems, v.23 n.2, p.107-143, September 2004 [doi>10.1023/B:JIIS.0000039532.05533.99]
• 7: Rogers, E. Diffusion of Innovations, 5th Edition. Free Press, New York, 2003.
• 8: 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]
• 9: Terveen, L. and Hill, W. Beyond Recommender Systems: Helping People Help Each Other. HCI In The New Millenium, Addison-Wesley, 2001.
• 10: Webster, A. and Vassileva, J. Push-Poll Recommender System: Supporting Word of Mouth. Proc. User Modeling 2007 (UM 2007), Springer-Verlag, Berlin, 2007, 288--297.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Design, Human Factors, Theory

Keywords:
RSS, collaborative tagging, online communities, social networks, social visualization