Ralf Krestel

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NN 15

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.

Full Paper

NN15.pdf
Download: ScienceDirect

BibTex Entry

@article{krestel-nn15, author = {Ralf Krestel and Nima Dokoohaki}, title = {{Diversifying Customer Review Rankings}}, journal ={Neural Networks}, volume = {66}, year = {2015}, pages = {36--45}, doi = {10.1016/j.neunet.2015.02.008}, issn = {0893-6080}, note = {Elsevier} }