ESTIMATION OF IMPLICIT USER INFLUENCE FROM PROXY LOGS - An Empirical Study on the Effects of Time Difference and Popularity

Tomonobu Ozaki, Minoru Etho

2011

Abstract

In this paper, we propose a framework for estimating implicit user influence from proxy logs. For the estimation, we employ a vector representation of user interactions obtained from log data by taking account of popularity of web pages and difference of access time to them. One of the key issues for successful estimation is how to model the popularity and time difference. Since appropriate models depend on application domains, we propose various models of them. We confirm the effectiveness of the proposed framework by conducting experiments on web page recommendation and community discovery for real proxy logs.

References

  1. Au Yeung, C.-m. and Iwata, T. (2010). Capturing implicit user influence in online social sharing. In Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, pages 245-254.
  2. Chang, C.-C. and Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/˜cjlin/libsvm.
  3. Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems:1695.
  4. Danon, L., Díaz-Guilera, A., Duch, J., and Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(9):P09008.
  5. Gomez Rodriguez, M., Leskovec, J., and Krause, A. (2010). Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1019-1028.
  6. Goyal, A., Bonchi, F., and Lakshmanan, L. V. (2010). Learning influence probabilities in social networks. In Proceedings of the third ACM International Conference on Web Search and Data Mining, pages 241-250.
  7. Kimura, M., Saito, K., and Motoda, H. (2009). Efficient estimation of influence functions for sis model on social networks. In Proceedings of the 21st International Joint Conference Artificial Intelligence, pages 2046- 2051.
  8. McPherson, M., Lovin, L. S., and Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1):415-444.
  9. Myers, S. and Leskovec, J. (2010). On the convexity of latent social network inference. In Advances in Neural Information Processing Systems 23, NIPS, pages 1741-1749.
  10. Newman, M. E. J. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2):026113.
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Paper Citation


in Harvard Style

Ozaki T. and Etho M. (2011). ESTIMATION OF IMPLICIT USER INFLUENCE FROM PROXY LOGS - An Empirical Study on the Effects of Time Difference and Popularity . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 242-247. DOI: 10.5220/0003659702500255


in Bibtex Style

@conference{kdir11,
author={Tomonobu Ozaki and Minoru Etho},
title={ESTIMATION OF IMPLICIT USER INFLUENCE FROM PROXY LOGS - An Empirical Study on the Effects of Time Difference and Popularity},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={242-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003659702500255},
isbn={978-989-8425-79-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - ESTIMATION OF IMPLICIT USER INFLUENCE FROM PROXY LOGS - An Empirical Study on the Effects of Time Difference and Popularity
SN - 978-989-8425-79-9
AU - Ozaki T.
AU - Etho M.
PY - 2011
SP - 242
EP - 247
DO - 10.5220/0003659702500255