Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2011)
Aug. 22, 2011 to Aug. 27, 2011
E-commerce Web sites owe much of their popularity to consumer reviews provided together with product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to build confidence in products they are planning to buy. At the same time, popular products have thousands of user-generated reviews. Current approaches to present them to the user or recommend an individual review for a product are based on the helpfulness or usefulness of each review. In this paper we look at the top-k reviews in a ranking to give a good summary to the user with each review complementing the others. To this end we use Latent Dirichlet Allocation to detect latent topics within reviews and make use of the assigned star rating for the product as an indicator of the polarity expressed towards the product and the latent topics within the review. We present a framework to cover different ranking strategies based on theuser's need: Summarizing all reviews, focus on a particular latent topic, or focus on positive, negative or neutral aspects. We evaluated the system using manually annotated review data from a commercial review Web site.
Ranking, Topic Models, Summarization, Diversification, Review Recommendation
Ralf Krestel, Nima Dokoohaki, "Diversifying Product Review Rankings: Getting the Full Picture", Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on, vol. 01, no. , pp. 138-145, 2011, doi:10.1109/WI-IAT.2011.33