Learning to recommend helpful hotel reviews
Published 23 October 2009Open access
Michael P. O’Mahony, Barry Smyth
Citations150
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A classification-based recommender system that is designed to recommend the most helpful reviews for a given product, and it is shown that this approach is capable of suggesting superior reviews compared to a number of alternative recommendation benchmarks.
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.
Keywords
Computer ScienceSocial Sciences
Elsevier eBooksData Mining: Practical Machine Learning Tools and Techniques
25,718 Citations2011Ian H. Witten, Eibe Frank
Explaining collaborative filtering recommendations
1,698 Citations2000Jonathan L. Herlocker, Joseph A. Konstan +1 more
This paper presents experimental evidence that shows that providing explanations can improve the acceptance of ACF systems, and presents a model for explanations based on the user's conceptual model of the recommendation process.
Malay LiteratureWhat's In A Name? Malay Seals As Onomastic Sources
1,510 Citations2018Annabel Teh Gallop
Automatically assessing review helpfulness
522 Citations2006Soo-Min Kim, Patrick Pantel +2 more
This paper considers the task of automatically assessing review helpfulness, and finds that the most useful features include the length of the review, its unigrams, and its product rating.
Expert Systems with ApplicationsA survey on sentiment detection of reviews
478 Citations2009Huifeng Tang, Songbo Tan +1 more
This survey discusses related issues and main approaches to these problems, namely, subjectivity classification, word sentiment classification, document sentiment classification and opinion extraction.
Modeling and Predicting the Helpfulness of Online Reviews
324 Citations2008Yang Liu, Xiangji Huang +2 more
This paper shows that the helpfulness of a review depends on three important factors: the reviewerpsilas expertise, the writing style of the review, and the timeliness of thereview, and presents a nonlinear regression model for helpfulness prediction.
Facts or friends?
262 Citations2009F. Maxwell Harper, Daniel Moy +1 more
The use of machine learning techniques are explored to automatically classify questions as conversational or informational, learning in the process about categorical, linguistic, and social differences between different question types.
Explaining Recommendations: Satisfaction vs. Promotion
252 Citations2005Mustafa Bilgic, Raymond J. Mooney
This work presents two new methods for explaining recommendations of contentbased and/or collaborative systems and experimentally shows that they actually improve user’s estimation of item quality.
Lecture notes in computer scienceMulti-facet Rating of Product Reviews
148 Citations2009Stefano Baccianella, Andrea Esuli +1 more
This work tackles the problem of rating (i.e., attributing a numerical score of satisfaction to) consumer reviews based on their textual content by exploring several aspects of the problem, with special emphasis on how to generate vectorial representations of the text by means of POS tagging, sentiment analysis, and feature selection for ordinal regression learning.
UvA-DARE (University of Amsterdam)Credibility Improves Topical Blog Post Retrieval
97 Citations2008Wouter Weerkamp, Maarten de Rijke
This work describes how to estimate textual credibility indicators and how to integrate them into a retrieval approach based on language models, and shows that both groups of credibility indicators significantly improve retrieval effectiveness.
Lecture notes in computer scienceThe Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data
41 Citations2008Nava Tintarev, Judith Masthoff
This paper studies the properties of a helpful and trustworthy explanation in a movie recommender system and finds that participants evaluated the explanations themselves most highly in the personalized, feature-based condition.