Abstract
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.
This work is supported by Science Foundation Ireland under Grant Nos. 07/CE/I1147 and 08/SRC/I1407.
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References
Bilgic, M., Mooney, R.J.: Explaining recommendations: Satisfaction vs. promotion. Beyond Personalization Workshop, held in conjunction with the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, USA (2005)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceeding on the ACM 2000 Conference on Computer Supported Cooperative Work, Philadelphia, PA, USA, pp. 241–250 (2000)
Gretzel, U., Fesenmaier, D.R.: Persuasion in recommender systems. International Journal of Electronic Commerce 11(2), 81–100 (2006)
O’Mahony, M.P., Smyth, B.: Learning to recommend helpful hotel reviews. In: 3rd ACM Conference on Recommender Systems (RecSys 2009) (2009)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Cunningham, P.: Dimension Reduction. In: Cord, M., Cunningham, P. (eds.) Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval, pp. 91–112. Springer, Heidelberg (2008)
Fawcett, T.: Roc graphs: Notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Laboratories, CA, USA (2004)
Weerkamp, W., de Rijke, M.: Credibility improves topical blog post retrieval. In: Proceedings of the Association for Computational Linguistics with the Human Language Technology Conference (ACL 2008: HLT), June 16-18, pp. 923–931 (2008)
Baccianella, S., Esuli, A., Sebastiani, F.: Multi-facet rating of product reviews. In: Boughanem, M., et al. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 461–472. Springer, Heidelberg (2009)
Harper, F.M., Moy, D., Konstan, J.A.: Facts or friends? Distinguishing informational and conversational questions in social Q&A sites. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI 2009), Boston, MA, USA, pp. 759–768 (April 2009)
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O’Mahony, M.P., Cunningham, P., Smyth, B. (2010). An Assessment of Machine Learning Techniques for Review Recommendation. In: Coyle, L., Freyne, J. (eds) Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science(), vol 6206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17080-5_26
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DOI: https://doi.org/10.1007/978-3-642-17080-5_26
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