Mining product adopter information from online reviews for improving product recommendation

Zhao, Wayne Xin, Wang, Jinpeng, He, Yulan, Wen, Ji-Rong, Chang, Edward Y. and Li, Xiaoming (2016). Mining product adopter information from online reviews for improving product recommendation. ACM Transactions on Knowledge Discovery from Data, 10 (3),

Abstract

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: https://doi.org/10.1145/2842629
Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied Sciences > Computer science research group
Uncontrolled Keywords: matrix factorisation,online review,product adopter,product recommendation,Computer Science(all)
Full Text Link: http://dl.acm.o ... 2888412.2842629
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2016-02-24
Authors: Zhao, Wayne Xin
Wang, Jinpeng
He, Yulan ( 0000-0003-3948-5845)
Wen, Ji-Rong
Chang, Edward Y.
Li, Xiaoming

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Version: Accepted Version


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