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Diversifying customer review rankings
University of California, US.
RISE, Swedish ICT, SICS.
2015 (English)In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 66, p. 36-45Article in journal (Refereed) Published
Abstract [en]

E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything. Current approaches to display reviews to users or recommend an individual review for a product are based on the recency or helpfulness of each review.In this paper, we present a framework to rank product reviews by optimizing the coverage of the ranking with respect to sentiment or aspects, or by summarizing all reviews with the top-K reviews in the ranking. To accomplish this, we make use of the assigned star rating for a product as an indicator for a review's sentiment polarity and compare bag-of-words (language model) with topic models (latent Dirichlet allocation) as a mean to represent aspects. Our evaluation on manually annotated review data from a commercial review Web site demonstrates the effectiveness of our approach, outperforming plain recency ranking by 30% and obtaining best results by combining language and topic model representations. 

Place, publisher, year, edition, pages
Elsevier Ltd , 2015. Vol. 66, p. 36-45
Keywords [en]
Diversification, Ranking, Review recommendation, Summarization, Topic models, Computational linguistics, Information retrieval, Social networking (online), Statistics, Websites, Review recommendations, Topic model, Sales, algorithm, Article, computer program, conceptual framework, consumer attitude, customer review, information model, information processing, language model, latent Dirichlet allocation, priority journal, sentiment focused ranking, summary focused ranking, web site, commercial phenomena, economic model, economics, human, Internet, postmarketing surveillance, Commerce, Humans, Models, Economic, Product Surveillance, Postmarketing
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Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-41877DOI: 10.1016/j.neunet.2015.02.008Scopus ID: 2-s2.0-84924942928OAI: oai:DiVA.org:ri-41877DiVA, id: diva2:1377787
Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2019-12-12Bibliographically approved

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CiteExportLink to record
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