Keyword-guided Topic-oriented Conversational Recommender System
Yiming Pan, Yunfei Yin, Faliang Huang · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
Conversational recommender system (CRS) allows agent to understand the conversation with user and give recommendations after multi-turn dialogues. However, there are still two limitations in existing CRS: (1) improper words or items may be chosen for the given topic in the generation, and (2) the contextual information of items in the recommendation is not rationally explored. To solve these issues, we proposed a Keyword-guided Topic-oriented CRS model (KGTO), which captures more accurate topic by extracting keywords through the hierarchical attention mechanism, and enriches the contextual information of items by fusing the co-occurrence graph with the knowledge graph. Moreover, a generative module can select words or items supplemented topic information to generate proper responses. Extensive experiments on the task-oriented dialogue dataset prove that our model performs well in recommendation effectiveness and dialogue informativeness.