亚洲AV

Bridging the Gap between Ratings and Reviews

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Online shopping is exploding, with more customers double-clicking instead of wandering store aisles. The drawback to online shopping is the inability to touch items, to feel the fabric or inspect the shoes. Do they look cheap? Do they run small? Is that vacation destination nice, or are the rooms stuffy? Online businesses rely on customer reviews to offer these answers.

Jingyuan Yang
Jingyuan Yang

User reviews often comprise two parts, the starred rating and the review.聽Jingyuan Yang, assistant professor of information systems and operations management, noticed a problem in that system. In her paper, 鈥淣euO: Exploiting the Sentimental Bias between Ratings and Reviews with Neural Networks鈥 (with coauthors Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, and Hui Xiong), she notes that often the review is missing or doesn鈥檛 match up with the rating. This gap is problematic because, she says, 鈥淚t is really important that users鈥 ratings and reviews be mutually reinforced to grasp the users鈥 true opinions.鈥

Yang and her coauthors exploited two-step training neural networks, using both reviews and ratings to grasp users鈥 true opinions. They developed an opinion-mining model using a specialized linear mathematical operation called convolution to ensure ratings. They used a combination function designed to catch the opinion bias and proposed a recommendation method using the enhanced user-item matrix.

Virtual businesses need healthy user reviews. When customers don鈥檛 feel they can rely on reviews, their trust in the company falters. Yang and her team have helped shore up the review system, an effort that will go a long way toward building happy customer bases.