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- Publisher
- Association for Computing Machinery
- Copyright
- Copyright © 2016 by ACM Inc.
- ISSN
- 1556-4681
- DOI
- 10.1145/2842629
- Publisher site
- See Article on Publisher Site
Abstract
Mining Product Adopter Information from Online Reviews for Improving Product Recommendation WAYNE XIN ZHAO, Renmin University of China JINPENG WANG, Peking University YULAN HE, Aston University JI-RONG WEN, Renmin University of China EDWARD Y. CHANG, HTC Research & Innovation XIAOMING LI, Peking University 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 for more 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 userproduct 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. CCS Concepts: r Information systems Information systems applications Additional
Journal
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Feb 9, 2016