Please use this identifier to cite or link to this item: https://doi.org/10.1115/DETC2011-48181
Title: Design preference centered review recommendation: A similarity learning approach
Authors: Jin, J.
Liu, Y. 
Ji, P.
Fung, R.
Issue Date: 2011
Source: Jin, J.,Liu, Y.,Ji, P.,Fung, R. (2011). Design preference centered review recommendation: A similarity learning approach. Proceedings of the ASME Design Engineering Technical Conference 2 (PARTS A AND B) : 1143-1152. ScholarBank@NUS Repository. https://doi.org/10.1115/DETC2011-48181
Abstract: The rise of e-commerce websites like Amazon and Alibaba is changing the way how designers seek information to identify customer preferences in product design. From the feedbacks posted by consumers, either positive or negative, product designers can monitor the trend of consumers' perception with respect to their product offerings and make efforts to improve accordingly. Starting from feature extraction from review documents, existing methods in identifying helpful online reviews regard the helpfulness prediction problem as a regression or classification problem and have not considered the relationship between customer reviews. Also, these approaches only consider the online helpfulness voting ratio or a unified helpfulness rating as the gold criteria for helpfulness evaluation and neglect various personal preferences from product designers. Therefore, in this paper, the focus is on how to predict reviews' helpfulness by taking into account the personal preferences from both reviewers and designers. We start to analyze review helpfulness from both a generic aspect and a personal preference aspect. Classification methods and the proposed review similarity learning approach are utilized to estimate from the generic angle of helpfulness, while nearest neighbourhood based methods are adopted to reflect concerns from personal perspectives. Finally, a regression algorithm is called upon to predict review helpfulness based on the inputs from both aspects. Our experimental study, using a large quantity of review data crawled from Amazon and real ratings from product designers demonstrates the effectiveness of our approach and it opens a possibility for customized helpful review routing. © 2011 by ASME.
Source Title: Proceedings of the ASME Design Engineering Technical Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/51573
ISBN: 9780791854792
DOI: 10.1115/DETC2011-48181
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