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Title: Multi-criteria and Review-Based Overall Rating Prediction
An overall rating cannot reveal the details of user’s preferences toward each feature of a product. One widespread practice of e-commerce websites is to provide ratings on predefined aspects of the product and user-generated reviews. Most recent multi-criteria works employ aspect preferences of users or user reviews to understand the opinions and behavior of users. However, these works fail to learn how users correlate these information sources when users express their opinion about an item. In this work, we present Multi-task & Multi-Criteria Review-based Rating (MMCRR), a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews. We conduct extensive experiments with three real-life datasets and six baseline models. The results show that MMCRR can reduce prediction errors while learning features better from the data.
Authors:
; ;
Award ID(s):
1633330 1914635 1757207
Publication Date:
NSF-PAR ID:
10302508
Journal Name:
Pacific-Asia Conference on Knowledge Discovery and Data Mining
Sponsoring Org:
National Science Foundation
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