TY - JOUR AU - Wang, Jinpeng AU - Zhao, Wayne AU - He, Yulan AU - Li, Xiaoming PY - 2021/08/03 Y2 - 2024/03/28 TI - Leveraging Product Adopter Information from Online Reviews for Product Recommendation JF - Proceedings of the International AAAI Conference on Web and Social Media JA - ICWSM VL - 9 IS - 1 SE - Full Papers DO - 10.1609/icwsm.v9i1.14585 UR - https://ojs.aaai.org/index.php/ICWSM/article/view/14585 SP - 464-472 AB - <p> 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. </p> ER -