Leveraging Product Adopter Information from Online Reviews for Product Recommendation


  • Jinpeng Wang Peking University
  • Wayne Zhao Renmin University of China
  • Yulan He Aston University
  • Xiaoming Li Peking University




recommender systems, product review mining, matrix factorization


The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are then categorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterise both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed framework for product recommendation.




How to Cite

Wang, J., Zhao, W., He, Y., & Li, X. (2021). Leveraging Product Adopter Information from Online Reviews for Product Recommendation. Proceedings of the International AAAI Conference on Web and Social Media, 9(1), 464-472. https://doi.org/10.1609/icwsm.v9i1.14585