Abstract
We propose a simple and efficient method that learns and assesses the conformity of each user of an online review system from the observed review score record. The model we use is a modified Voter model that takes account of the conformity of each user. Conformity is learnable quite efficiently with a few tens of iterations by maximizing the log-likelihood given the observed data. The proposed method was evaluated and confirmed effective by two review datasets. It could identify both high and low conformity users. Users with high conformity are not necessarily early adopters. Their scores are influential to drive the consensus score. The user ranking of conformity was compared with Page Rank and HITS in which user network was roughly approximated by the directed graph induced by the observed data. The proposed method gives more interpretable ranking, and the global property of high conformity users was identified.
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References
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30, 107–117 (1998)
Even-Dar, E., Shapira, A.: A note on maximizing the spread of influence in social networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 281–286. Springer, Heidelberg (2007)
Jamali, M., Ester, A.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems (RecSys 2010), pp. 135–142 (2010)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)
Koren, Y.: Factorization meets the neighborhood: a mulifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 426–434 (2008)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development (SIGIR 2009), pp. 203–210 (2009)
Ma, H., Yang, H., Lyu, M.R., King, I.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM 2008), pp. 931–940 (2008)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM 2011), pp. 287–296 (2011)
Michael, L., Otterbacher, J.: Write like I write: herding in the language of online reviews. In: Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (ICWSM 2014), pp. 356–365 (2014)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems 20 (NIPS 2007), pp. 791–798 (2008)
Sood, V., Redner, S.: Voter model on heterogeneous graphs. Physical Review Letters 94, 178701 (2005)
Tang, J., Hu, X., Gao, H., Liu, H.: Exploiting local and global social context for recommendation. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), pp. 2712–2718 (2013)
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Saito, K., Ohara, K., Kimura, M., Motoda, H. (2015). Efficient Learning of User Conformity on Review Score. In: Agarwal, N., Xu, K., Osgood, N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science(), vol 9021. Springer, Cham. https://doi.org/10.1007/978-3-319-16268-3_19
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DOI: https://doi.org/10.1007/978-3-319-16268-3_19
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