Leveraging Historical Interaction Data for Improving Conversational Recommender System

• URL: http://arxiv.org/abs/2008.08247v1
• Date: Wed, 19 Aug 2020 03:43:50 GMT
• Title: Leveraging Historical Interaction Data for Improving Conversational Recommender System
• Authors: Kun Zhou, Wayne Xin Zhao, Hui Wang, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang and Ji-Rong Wen
• Abstract summary: We propose a novel pre-training approach to integrate item- and attribute-based preference sequence. Experiment results on two real-world datasets have demonstrated the effectiveness of our approach.
• Score: 105.90963882850265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, conversational recommender system (CRS) has become an emerging and practical research topic. Most of the existing CRS methods focus on learning effective preference representations for users from conversation data alone. While, we take a new perspective to leverage historical interaction data for improving CRS. For this purpose, we propose a novel pre-training approach to integrating both item-based preference sequence (from historical interaction data) and attribute-based preference sequence (from conversation data) via pre-training methods. We carefully design two pre-training tasks to enhance information fusion between item- and attribute-based preference. To improve the learning performance, we further develop an effective negative sample generator which can produce high-quality negative samples. Experiment results on two real-world datasets have demonstrated the effectiveness of our approach for improving CRS.

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