Building community-centric information exploration applications on social content sites

ABSTRACT

Social content sites [4], which integrate traditional content sites with social networking features, have recently emerged as an exciting new trend on the Web. Users on those sites share content and form various communities based on explicit friendship, shared interest and common user properties. Recently, we proposed SOCIALSCOPE, a three-layered architecture to address the information management challenges in social content sites. In this paper, we focus on the information discovery and the information presentation layers, and describe how our previously proposed language, Jelly [3], is supported in SOCIALSCOPE to build community-centric information exploration applications on social content sites.

REFERENCES

• 1: Gediminas Adomavicius , Alexander Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, v.17 n.6, p.734-749, June 2005 [doi>10.1109/TKDE.2005.99]
• 2: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 3: Sihem Amer-Yahia , Jian Huang , Cong Yu, Jelly: A Language for Building Community-Centric Information Exploration Applications, Proceedings of the 2009 IEEE International Conference on Data Engineering, p.1588-1594, March 29-April 02, 2009 [doi>10.1109/ICDE.2009.178]
• 4: S. Amer-Yahia, L. Lakshmanan, and C. Yu. SocialScope: Enabling Information Discovery on Social Content Sites. In CIDR, 2009.
• 5: M. Bilgic and R. Mooney. Explaining recommendations: Satisfaction vs. promotion. Beyond Personalization Workshop. In IUI, 2005.
• 6: D. M. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tenenbaum. Hierarchical topic models and the nested chinese restaurant process. In Advances in Neural Information Processing Systems, page 2003, 2004.
• 7: David M. Blei , Andrew Y. Ng , Michael I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3, p.993-1022, 3/1/2003
• 8: 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]
• 9: J. A. Konstan. Introduction to recommender systems. In SIGIR, 2007.
• 10: Christopher Olston , Benjamin Reed , Utkarsh Srivastava , Ravi Kumar , Andrew Tomkins, Pig latin: a not-so-foreign language for data processing, Proceedings of the 2008 ACM SIGMOD international conference on Management of data, June 09-12, 2008, Vancouver, Canada [doi>10.1145/1376616.1376726]
• 11: Pearl Pu , Li Chen, Trust building with explanation interfaces, Proceedings of the 11th international conference on Intelligent user interfaces, January 29-February 01, 2006, Sydney, Australia [doi>10.1145/1111449.1111475]
• 12: 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]
• 13: W3C. SPARQL Query Language for RDF. www.w3.org/TR/rdf-sparql-query.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.m Miscellaneous

General Terms:
Languages

Keywords:
declarative languages, recommender systems, search, social content sites