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Learning to recommend helpful hotel reviews
Author(s)
Date Issued
2009-10
Date Available
2010-03-29T15:43:20Z
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.
is capable of suggesting superior reviews compared to a number of alternative recommendation benchmarks.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2009 ACM
Subject – LCSH
Recommender systems (Information filtering)
Automatic classification
User-generated content--Classification
Web versions
Language
English
Status of Item
Peer reviewed
Journal
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
ISBN
978-1-60558-435-5
This item is made available under a Creative Commons License
File(s)
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sp235-omahony.pdf
Size
249.29 KB
Format
Adobe PDF
Checksum (MD5)
98a5324208b41367c341904e470c11a8
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