title: Diversifying customer review rankings.
abstract: 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.
authors: Department of Computer Science, University of California, Irvine, USA. Electronic address: krestel@L3S.de.
authors: Ralf
authors: R
authors: Krestel
authors: Decisions, Networks and Analytics (DNA), Swedish Institute of Computer Science (SICS), Stockholm, Sweden.
authors: Nima
authors: N
authors: Dokoohaki
mesh_terms: D003132:Commerce / Q000191:economics*; D006801:Humans; D020407:Internet; D018803:Models, Economic; D011358:Product Surveillance, Postmarketing
keywords: Diversification; Ranking; Review recommendation; Summarization; Topic models