Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Machine Learning
Persuasion in Recommender Systems
International Journal of Electronic Commerce
Facts or friends?: distinguishing informational and conversational questions in social Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multi-facet Rating of Product Reviews
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Learning to recommend helpful hotel reviews
Proceedings of the third ACM conference on Recommender systems
A classification-based review recommender
Knowledge-Based Systems
Using readability tests to predict helpful product reviews
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Neighborhood random classification
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Topic extraction from online reviews for classification and recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this paper, we consider a classification-based approach to the recommendation of user-generated product reviews. In particular, we develop review ranking techniques that allow the most helpful reviews for a particular product to be recommended, thereby facilitating users to readily asses the quality of the product in question. We apply a supervised machine learning approach to this task and compare the performance achieved by several classification algorithms using a large-scale study based on TripAdvisor hotel reviews. Our findings indicate that our approach is successful in recommending helpful reviews compared to benchmark ranking schemes, and further we highlight an interesting performance asymmetry that is biased in favour of reviews expressing negative sentiment.