Toward personalized answer generation in e-commerce via multi-perspective preference modeling

• Main Authors: DENG, Yang, LI, Yaliang, ZHANG, Wenxuan, DING, Bolin, LAM, Wai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Answer generation; product question answering; personalization; E-Commerce; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9090; https://ink.library.smu.edu.sg/context/sis_research/article/10093/viewcontent/3507782.pdf
• Institution: Singapore Management University

Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience. Its key function, automatic answer generation for product-related questions, has been studied by aiming to generate content-preserving while question-related answers. However, an important characteristic of PQA, i.e., personalization, is neglected by existing methods. It is insufficient to provide the same “completely summarized” answer to all customers, since many customers are more willing to see personalized answers with customized information only for themselves, by taking into consideration their own preferences toward product aspects or information needs. To tackle this challenge, we propose a novel Personalized Answer GEneration method with multi-perspective preference modeling, which explores historical user-generated contents to model user preference for generating personalized answers in PQA. Specifically, we first retrieve question-related user history as external knowledge to model knowledge-level user preference. Then, we leverage the Gaussian Softmax distribution model to capture latent aspect-level user preference. Finally, we develop a persona-aware pointer network to generate personalized answers in terms of both content and style by utilizing personal user preference and dynamic user vocabulary. Experimental results on real-world E-Commerce QA datasets demonstrate that the proposed method outperforms existing methods by generating informative and customized answers and show that answer generation in E-Commerce can benefit from personalization.