Mining product adopter information from online reviews for improving product recommendation


We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
?? 50811700Jl ??
Uncontrolled Keywords: matrix factorisation,online review,product adopter,product recommendation,General Computer Science
Publication ISSN: 1556-472X
Last Modified: 27 Jun 2024 08:49
Date Deposited: 25 May 2016 09:28
Full Text Link: http://dl.acm.o ... 2888412.2842629
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2016-02-24
Accepted Date: 2015-11
Submitted Date: 2015-07
Authors: Zhao, Wayne Xin
Wang, Jinpeng
He, Yulan (ORCID Profile 0000-0003-3948-5845)
Wen, Ji-Rong
Chang, Edward Y.
Li, Xiaoming



Version: Accepted Version

Export / Share Citation


Additional statistics for this record