Employer-Employee Network for Conversational Recommendation

Traditional recommendation systems model user preferences based on past historical behaviors, thus unable to obtain dynamic user preferences. The conversational recommendation system (CRS) combines the conversational module with the recommendation module and overcomes the limitations by directly asking the user's preference for attributes. However, the existing CRS methods lack effective information propagation among various modules, making the model lack the basis for making correct decisions. In this paper, we propose an Employer-Employee network, which decomposes the actions into two stages, which are completed by two networks respectively. The Employer Network is responsible for analyzing information and making decisions (query or recommend), and the Employee Network is responsible for collecting information and performing tasks. Our contributions can be highlighted in three aspects: We first emphasize the importance of information propagation among multiple modules in the conversational recommendation system. Secondly, we propose an Employer-Employee (EE) network, which transforms each turn of action into a two-stage decision-making task handed over to two networks to complete. Thirdly, we conduct experiments on multiple datasets, and the experimental results show that our model achieves competitive performance compared with state-of-the-art baselines.