Conversational Recommendations With User Entity Focus and Multi-Granularity Latent Variable Enhancement

Conversational recommendation is one system that can extract the user's preferences and recommend suitable items in a similar way to human-like responses. Existing methods often use the feature extraction combined with the Transformer model to extract user preferences and make recommendations. However, these methods have two limitations. First, they do not consider the order in which entities appear, thus affecting the extraction of user preferences. Second, the generated responses lack diversity that affects the users’ experience to the system. To this end, we propose a conversational recommendation model with User Entity focus and Multi-Granularity latent variable enhancement (UEMG). In UEMG, we design a novel neural network that utilizes Bi-GRU to capture the appearing orders of entities in dialogues, and leverages Transformer to capture the global dependencies of entities, and then combines them to extract user preferences. For the second issue, to improve the diversity of dialogue generation, we propose a multi-granularity latent variable mechanism, which can extract more entities from the context information and the knowledge graphs, respectively. We conducted extensive experiments on publicly available dialogue generation datasets. Experimental results demonstrate that compared to current state-of-the-art methods, UEMG achieves 9.7% improvements in recommendation performance and 23% improvements in dialogue generation.