IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Modeling online reviews with multi-grain topic models
Proceedings of the 17th international conference on World Wide Web
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Capturing the stars: predicting ratings for service and product reviews
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
Rating prediction using feature words extracted from customer reviews
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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We propose a novel method to improve the prediction accuracy on the rating prediction task by correcting the bias of user ratings. We demonstrate that the manner of user rating and review is biased and that it is necessary to correct this difference for more accurate prediction. Our proposed method comprises approaches based on the detection of each user value to ratings: The bias of the rating is detected using entropy of user rating and by updating word weights only when the words appear in the review, the problem of bias is reduced. We implement this idea by extending the Prank algorithm. We apply a review -- item matrix as a feature matrix instead of a user -- item matrix because of its volume of information. Our quantitative evaluation shows that our method improves the prediction accuracy (the Rank Loss measurement) significantly by 8.70 % compared with the normal Prank algorithm. Our proposed method helps users find out what they care about when buying something, and is applicable to newer variants of the Prank algorithm. Moreover, it is useful to most review sites because we use only rating and review data.