Rating Prediction and Suggestion for Quality Improvement Based on User Feedback.
Vinodhini R, Sabaresan V, Vairavi S
Online reviews became a vital supply of knowledge for users before creating associate well-read purchase call. Early reviews of a product tend to possess a high impact on the following product sales. during this paper,we have a tendency to take the initiative to check the behavior characteristics of early reviewers through their denote reviews on 2 real-world massive e-commerce platforms, i.e., Amazon and Yelp. In specific, we have a tendency to divide product time period into 3 consecutive stages, particularly early, majority and laggards. A user World Health Organization has denote a review within the early stage is taken into account as associate early reviewer. we have a tendency to quantitatively characterize early reviewers supported their rating behaviors, the helpfulness scores received from others and also the correlation of their reviews with product quality.we've got found that (1) associate early reviewer tends to assign a better average rating score; associated (2) an early reviewer tends to post additional useful reviews. Our analysis of product reviews conjointly indicates that early reviewers’ ratings and their received helpfulness scores area unit probably to influence product quality. By viewing review posting method as a multiplayer competition game, we have a tendency to propose a completely unique margin-based embedding model for early reviewer prediction. Intensive experiments on 2 completely different e-commerce datasets have shown that our planned approach outperforms variety of competitive baselines.
Article Details
Unique Paper ID: 150906

Publication Volume & Issue: Volume 7, Issue 10

Page(s): 332 - 334
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