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Recommender Systems from "Words of Few Mouths"

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 2379-2383.

https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-396

Abstract

This paper identifies a widely existing phenomenon in web data, which we call the words of few mouths phenomenon. This phenomenon, in the context of online reviews, refers to the case that a large fraction of the reviews are each voted only by very few users. We discuss the challenges of words of few mouths in the development of recommender systems based on users' opinions and advocate probabilistic methodologies to handle such challenges. We develop a probabilistic model and correspondingly a logistic regression based learning algorithm for review helpfulness prediction. Our experimental results indicate that the proposed model outperforms the current state-of-the-art algorithms not only in the presence of the words of few mouths phenomenon, but also in the absence of such phenomena.

Richong Zhang, Thomas T. Tran, and Yongyi Mao. 2011. Recommender Systems from "Words of Few Mouths". In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 2379–2383. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-396
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