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Conversation and recommendation: knowledge-enhanced personalized dialog system

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Traditional recommender systems are usually single-shot systems, lacking real-time dialog with customers. Using dialog as an interactive method can help capture user preferences more accurately and enhance system transparency. However, developing such a goal-oriented dialog system has suffered many challenges as the system must collaborate with other subtasks, such as collecting user demands through interaction and recommending appropriate products to users. Additionally, most previous studies on dialog systems do not consider this situation and its challenges. This paper proposes a novel memory network framework for conversational recommendation, which harnesses dialog historical information to endow our model with adaptability in various dialog scenarios. Additionally, it leverages the knowledge base and user profiles to reweight candidates, reducing the ambiguity during interactions and improving the quality of conversational recommender systems. We demonstrate that the proposed method can achieve state-of-the-art performance in a few traditional tasks, such as options display and information provision, through experiments on the personalized bAbI dialog dataset and restaurant recommendation application.

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This work is supported in part by the Beijing Natural Science Foundation under grant 4192008.

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Correspondence to Ming He.

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He, M., Wang, J., Ding, T. et al. Conversation and recommendation: knowledge-enhanced personalized dialog system. Knowl Inf Syst 65, 261–279 (2023).

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