Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling
This addresses the challenge of improving recommendation accuracy in conversational systems for users, though it is incremental as it builds on existing Transformer and knowledge graph methods.
The paper tackles the problem of modeling sequential dependencies in conversational recommender systems (CRSs) by proposing TSCR, a Transformer-based method that represents conversations with items and entities and uses a Cloze task for prediction, and TSCRKG, an enhanced version that incorporates knowledge graphs, with experimental results showing significant outperformance over state-of-the-art baselines and further improvements with TSCRKG.
In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the development of a dialog, leading to potential sequential dependencies among them. However, most of existing CRSs neglect these potential sequential dependencies. In this article, we first propose a Transformer-based sequential conversational recommendation method, named TSCR, to model the sequential dependencies in the conversations to improve CRS. In TSCR, we represent conversations by items and the item-related entities, and construct user sequences to discover user preferences by considering both the mentioned items and item-related entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Meanwhile, in certain domains, knowledge graphs formed by the items and their related entities are readily available, which provide various different kinds of associations among them. Given that TSCR does not benefit from such knowledge graphs, we then propose a knowledge graph enhanced version of TSCR, called TSCRKG. In specific, we leverage the knowledge graph to offline initialize our model TSCRKG, and augment the user sequence of conversations (i.e., sequence of the mentioned items and item-related entities in the conversation) with multi-hop paths in the knowledge graph. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines, and the enhanced version TSCRKG further improves recommendation performance on top of TSCR.