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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Value-Oriented Ranking of Online Reviews Based on Reviewer-Influenced Graph
verfasst von : Yiming Cao, Lizhen Cui, Wei He
Erschienen in: Database Systems for Advanced Applications
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Abstract
To mitigate the uncertainty of online purchases, people rely on reviews written by customers who already bought the product to make their decisions. The key challenge in this situation is how to identify the most helpful reviews among a large number of candidate reviews with different quality. Existing work normally employs diversified text and sentiment analysis algorithms to analyze the helpfulness of reviews. Voting on reviews is another popular valuation way adopted by many websites, which also has difficulties to reflect the real helpfulness of the reviews due to the problem of data sparseness. In this paper, a reviewer-influenced graph model is constructed based on the reviewers’ historical reviews and voting information to measure the influence of reviewers’ quality on the helpfulness of reviews. Experimental results with actual review data from Amazon.com demonstrate the effectiveness of our approach.