Deep Conversational Recommender in Travel
TABLE III: Performance comparison of recommenders.
['[BOLD] Methods', '[BOLD] ReDial', '[BOLD] NCF', '[BOLD] GCN-based']
Since often only the top item is leveraged in the dialogs, we report the Top-1 accuracy here. It shows that the GCN-based recommender component achieves better performance as compared to ReDial and NCF methods. For the ReDial recommender, it projects the entity appearance vector v of each dialog session into a smaller vector space, then retrieve a new entity vector v′ with same dimension to minimize the difference between them. It only models the co-occurrence relationship among entities. The entity information and the dialog context information are largely ignored. At the same time, the entity co-occurrence matrix formed via training dialog sessions is rather sparse. These factors together lead to its relatively weak performance. Regarding the NCF method, the dialog contexts are gathered via HRED to form vector representations of users. We adopt a multi-layer perceptron (MLP) to learn the interaction between user and item features. Still, the various relationships between venue entities are not modeled. On the contrary, the GCN-based recommender component in DCR manages to handle all the three evidence sources — the venue information, relations between them and the match to dialog context.