论文标题
对话推荐的用户记忆推理
User Memory Reasoning for Conversational Recommendation
论文作者
论文摘要
我们研究了一个会话推荐模型，该模型通过结构化和累积的用户内存知识图动态管理用户的过去（离线）偏好和当前（在线）请求，以提供自然的交互和准确的建议。在这项研究中，我们创建了一个新的记忆图（MG）<->对话推荐并行语料库，称为MGCONVREX，具有7K+人类对人类角色扮演的对话框，该对话框基于由现实世界中用户场景的大规模用户记忆自动化。 MGCONVREX通过用户内存捕获了人级推理，并具有零射击（冷启动）推理的用户的不相交培训/测试集。我们提出了一种简单但可扩展的公式，用于构建和更新MG，以及一个预测无约束图空间中最佳对话策略和建议项目的推理模型。我们提出的模型的预测继承了图形结构，提供了一种自然的方式来解释该模型的建议。对离线指标和在线模拟进行了实验，显示了竞争成果。
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.
