Research Area: Machine Learning
In Conversational Recommender Systems (CRSs), conversations usually involve a set of related items and entities e.g., attributes of items. These items and entities are mentioned in order following the development of a dialogue. In other words, potential sequential dependencies exist in conversations. However, most of the existing CRSs neglect these potential sequential dependencies. In this paper, we propose a Transformer-based sequential conversational recommendation method, named TSCR, which models the sequential dependencies in the conversations to improve CRS. We represent conversations by items and entities, and construct user sequences to discover user preferences by considering both mentioned items and entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines.
Keywords:
Conversational Recommender Systems
Transformer-based
Sequential Modelling
TSCR model
Author(s) Name: Jie Zou , Evangelos Kanoulas , Pengjie Ren , Zhaochun Ren
Journal name:
Conferrence name: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher name: ACM Library
DOI: 10.1145/3477495.3531852
Volume Information:
