An SVD-Entropy and Bilinearity based Product Ranking Algorithm using Heterogeneous Data

Abstract

E-commerce websites, besides selling products and services, pay ample emphasis on providing a platform for consumers to share their opinions about past and potential purchases. They share such opinions as product reviews (star ratings, plain text, etc.) and answering product related questions (Q&A data). There are several machine learning and classification approaches available to scrutinize this review data, e.g., algorithms based on Entropy measures, Bilinear Similarity, stochastic methods, etc. In this paper, we review some of the prevalent review classification techniques and present a hybrid approach, involving Singular Value Decomposition (SVD), Entropy and Bilinear Similarity measures, that uses heterogeneous product data and simultaneously analyze and rank products for customers. With experimental results, we show that our approach effectively ranks products using (1) text reviews (2) Q&A data (3) five-star rating of products and has 10% improved prediction accuracy as compared to the individual approaches. Also, using SVD, we achieve a 35% runtime efficiency for our algorithm while only sacrificing 1% of the prediction accuracy.

Department(s)

Computer Science

Keywords and Phrases

Bilinear similarity; Entropy; Product ranking; Product review; SVD

International Standard Serial Number (ISSN)

1045-926X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Academic Press, All rights reserved.

Publication Date

01 Aug 2017

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