Diversifying customer review rankings

• 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. 

Keyword

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

Publication and Content Type

ref (subject category)

art (subject category)