Mining User Preferences from Online Reviews with the Genre-aware Personalized neural Topic Model

Rui Wang, Jiahao Lu, Xincheng Lv, Shuyu Chang, Yansheng Wu, Yuanzhi Yao, Haiping Huang, Guozi Sun

Track: Web mining and content analysis

Keywords: Neural Topic Modeling, User Preferences Discovery, Text Mining

TL;DR: We propose a Genres-aware Personalized Topic Model for user preference modeling and personalized topic modeling.

Abstract: Customer-generated reviews on e-commerce websites often contain valuable insights into users' interests in product genres and provide a rich source for mining user preferences. However, most existing neural topic models tend to generate meaningless topics that have low correlations with product genres. Furthermore, they often fail to mine user preferences and discover personalized topic profiles due to the absence of explicit user modeling. To address these limitations, we propose a novel Genre-aware Personalized neural Topic Model (GPTM), which incorporates product genre information into the topic modeling process to ensure the relevance between mined topics and product genres. Moreover, it could produce a personalized topic profile for each user by performing user preference modeling. Extensive experimental results on three publicly available Amazon review corpora validate the effectiveness of the proposed GPTM in genre-aware topic modeling. Furthermore, GPTM surpasses state-of-the-art baselines in user preference mining and generating high-quality personalized topic profiles.

Submission Number: 2800