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.

本文介绍一种基于用户记忆知识图谱的对话推荐模型，在大规模用户场景数据的基础上构建了一种新的记忆图谱，并提出了一种简单可扩展的构建和更新图谱的公式，该模型预测出最佳对话策略和推荐项，并提供自然的方式解释模型的推荐，同时也验证了模型在离线和在线模拟中具有竞争力的效果。