Neural networks for pattern recognition
Neural networks for pattern recognition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A helpfulness modeling framework for electronic word-of-mouth on consumer opinion platforms
ACM Transactions on Intelligent Systems and Technology (TIST)
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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.