Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Facts or friends?: distinguishing informational and conversational questions in social Q&A sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Learning to recommend helpful hotel reviews
Proceedings of the third ACM conference on Recommender systems
An assessment of machine learning techniques for review recommendation
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
Estimating sequential bias in online reviews: A Kalman filtering approach
Knowledge-Based Systems
RESYGEN: A Recommendation System Generator using domain-based heuristics
Expert Systems with Applications: An International Journal
Identifying the semantic orientation of terms using S-HAL for sentiment analysis
Knowledge-Based Systems
The development of intuitive knowledge classifier and the modeling of domain dependent data
Knowledge-Based Systems
Identifying influential nodes in complex networks with community structure
Knowledge-Based Systems
Incorporating group recommendations to recommender systems: Alternatives and performance
Information Processing and Management: an International Journal
Recommendation using textual opinions
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
Many online stores encourage their users to submit product or service reviews in order to guide future purchasing decisions. These reviews are often listed alongside product recommendations but, to date, limited attention has been paid as to how best to present these reviews to the end-user. In this paper, we describe a supervised classification approach that is designed to identify and recommend the most helpful product reviews. Using the TripAdvisor service as a case study, we compare the performance of several classification techniques using a range of features derived from hotel reviews. We then describe how these classifiers can be used as the basis for a practical recommender that automatically suggests the most-helpful contrasting reviews to end-users. We present an empirical evaluation which shows that our approach achieves a statistically significant improvement over alternative review ranking schemes.