An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Text similarity: an alternative way to search MEDLINE
Bioinformatics
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Understanding user behavior in online feedback reporting
Proceedings of the 8th ACM conference on Electronic commerce
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Information shared by many objects
Proceedings of the 17th ACM conference on Information and knowledge management
IEEE Transactions on Information Theory
International Journal of Intelligent Systems
A multidimensional data model using the fuzzy model based on the semantic translation
Information Systems Frontiers
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On participatory Websites, users provide opinions about products, with both overall ratings and textual reviews. In this paper, we propose an approach to accurately estimate feature ratings of the products. This approach selects user reviews that extensively discuss specific features of the products (called specialized reviews), using information distance of reviews on the features. Experiments on real data show that overall ratings of the specialized reviews can be used to represent their feature ratings. The average of these overall ratings can be used by recommender systems to provide feature specific recommendations that better help users make purchasing decisions.