User Memory Reasoning for Conversational Recommendation

• URL: http://arxiv.org/abs/2006.00184v1
• Date: Sat, 30 May 2020 05:29:23 GMT
• Title: User Memory Reasoning for Conversational Recommendation
• Authors: Hu Xu, Seungwhan Moon, Honglei Liu, Bing Liu, Pararth Shah, Bing Liu, Philip S. Yu
• Abstract summary: We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests. MGConvRex captures human-level reasoning over user memory and has disjoint training/testing sets of users for zero-shot (cold-start) reasoning for recommendation.
• Score: 68.34475157544246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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