Identifying User and Group Information from Collaborative Filtering Data Sets

Josephine Griffith, Colm O’Riordan, Humphrey Sorensen

Abstract

This paper considers the information that can be captured about users and groups from a collaborative filtering data set with a view to creating user models and group models. The approach outlined defines a number of user and group features which are represented using a graph model where links exist between users and items, between users and users, and between items and items. The main focus of this paper is to extract implicit information about users and groups that exists in a collaborative filtering data set.

References

  1. Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating word of mouth. In: Proceedings of the Annual ACM SIGCHI on Human Factors in Computing Systems (CHI 7895). (1995) 210-217
  2. Barnes, J.: Social Networks. MA: Addison-Wesley (1972)
  3. Vivacqua, A., Lieberman, H.: Agents to assist in finding help. In: ACM Conference on Computers and Human Interface (CHI-2000). (2000)
  4. McDonald, D., Ackerman, M.: Expertise recommender: a flexible recommendation system and architecture. In: Proceedings of the 2000 ACM conference on Computer supported cooperative work. (2000) 231 - 240
  5. Krulwich, B., Burkey, C.: The contactfinder: Answering bulletin board questions with referrals. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence. (1996)
  6. Schwartz, M., Wood, D.: Discovering shared interests using graph analysis. Communications of the ACM 36 (1993) 78 - 89
  7. Kautz, H., Selman, B., Shah, M.: Referral web: combining social networks and collaborative filtering. Communications of the ACM 40 (1997) 63 - 65
  8. Mirza, B., Keller, B., Ramakrishnan, N.: Studying recommendation algorithms by graph analysis. Journal of Intelligent Information Systems 20 (March 2003) 131 - 160
  9. Rashid, A., Karypis, G., Riedl, J.: Influence in ratings-based recommender systems: An algorithm-independent approach. In: SIAM International Conference on Data Mining. (2005)
  10. Lemire, D.: Scale and translation invariant collaborative filtering systems. Information Retrieval 8 (2005) 129-150
  11. Palau, J., Montaner, M., Lpez, B.: Collaboration analysis in recommender systems using social networks. In: Cooperative Information Agents VIII: 8th International Workshop, CIA 2004. (2004) 137 - 151
  12. Aggarwal, C., Wolf, J., Wu, K.L., Yu, P.: Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In: Proceedings of the Fifth ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'99), San Diego, CA. (1999) 201-212
  13. Huang, Z., Chung, W., Chen, H.: A graph model for e-commerce recommender systems. Journal of the American Society for Information Science and Technology 55 (2004) 259- 274
  14. Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems 22 (2004) 116-142
  15. Mukhopadhyay, U., Stephens, L., Huhns, M., Bonnell, R.: An intelligent system for document retrieval in distributed office environments. Journal of the American Society for Information Science 37 (1986)
  16. Herlocker, J., Konstan, J., Riedl, J.: An empirical analysis of design choices in neighbourhood-based collaborative filtering algorithms. Information Retrieval 5 (2002) 287- 310
  17. Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: Proceedings of SIAM Data Mining (SDM'05). (2005)
  18. Freeman, L.: Centrality in social networks: Conceptual clarification. Social Networks 1 (1979) 215-239
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Paper Citation


in Harvard Style

Griffith J., O’Riordan C. and Sorensen H. (2005). Identifying User and Group Information from Collaborative Filtering Data Sets . In Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005) ISBN 972-8865-38-4, pages 98-106. DOI: 10.5220/0001421600980106


in Bibtex Style

@conference{wprsiui05,
author={Josephine Griffith and Colm O’Riordan and Humphrey Sorensen},
title={Identifying User and Group Information from Collaborative Filtering Data Sets},
booktitle={Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005)},
year={2005},
pages={98-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001421600980106},
isbn={972-8865-38-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005)
TI - Identifying User and Group Information from Collaborative Filtering Data Sets
SN - 972-8865-38-4
AU - Griffith J.
AU - O’Riordan C.
AU - Sorensen H.
PY - 2005
SP - 98
EP - 106
DO - 10.5220/0001421600980106