标题：Value-Oriented Ranking of Online Reviews Based on Reviewer-Influenced Graph
作者：Cao, Yiming; Cui, Lizhen; He, Wei
通讯作者：Cui, Lizhen;Cui, LZ
作者机构：[Cao, Yiming; Cui, Lizhen; He, Wei] Shandong Univ, Jinan, Shandong, Peoples R China.
会议名称：24th Int Conference on Database Systems for Advanced Applications / 6th Int Workshop on Big Data Management and Service / 4th Int Workshop on Big Data Quality Management / 3rd Int Workshop on Graph Data Management and Analysis
会议日期：APR 22-25, 2019
来源：DATABASE SYSTEMS FOR ADVANCED APPLICATIONS
关键词：Reviewer-influenced graph model; Reviewer quality; Reviewer-influenced; helpfulness
摘要：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.