Learning to recommend helpful hotel reviews
|Title:||Learning to recommend helpful hotel reviews||Authors:||O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/1894||Date:||Oct-2009||Abstract:||User-generated reviews are a common and valuable source of product information, yet little attention has been paid as to how best to present them to end-users. In this paper, we describe a classification-based recommender system that is designed to recommend the most helpful reviews for a given product. We present a large-scale evaluation of our approach using TripAdvisor hotel reviews, and we show that our approach is capable of suggesting superior reviews compared to a number of alternative recommendation benchmarks.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||ACM||Copyright (published version):||2009 ACM||Keywords:||Review Recommendation;Classification;TripAdvisor||Subject LCSH:||Recommender systems (Information filtering)
|DOI:||10.1145/1639714.1639774||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings of the third ACM conference on Recommender systems||Conference Details:||Paper presented at the 3rd ACM Conference on Recommender Systems (RecSys 2009), New York City, NY, USA, 22-25 October 2009|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
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