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arXiv · 2006.00184

User Memory Reasoning for Conversational Recommendation

Hu Xu·Seungwhan Moon·Honglei Liu·Bing Liu·Pararth Shah·Philip S. Yu
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Abstract

We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph, to allow for natural interactions and accurate recommendations. For this study, we create a new Memory Graph (MG) <--> Conversational Recommendation parallel corpus called MGConvRex with 7K+ human-to-human role-playing dialogs, grounded on a large-scale user memory bootstrapped from real-world user scenarios. MGConvRex captures human-level reasoning over user memory and has disjoint training/testing sets of users for zero-shot (cold-start) reasoning for recommendation. We propose a simple yet expandable formulation for constructing and updating the MG, and a reasoning model that predicts optimal dialog policies and recommendation items in unconstrained graph space. The prediction of our proposed model inherits the graph structure, providing a natural way to explain the model's recommendation. Experiments are conducted for both offline metrics and online simulation, showing competitive results.

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Hu Xu, Seungwhan Moon, Honglei Liu, Bing Liu, Pararth Shah, Philip S. Yu. 2020-05-30. User Memory Reasoning for Conversational Recommendation. https://arxiv.org/abs/2006.00184

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