用问答能力定义历史表示，让模型更懂用户偏好。
Descriptive History Representations: Learning Representations by Answering Questions
- 以回答任务相关问题的能力来学习历史表征
- 在电影和购物数据上生成可解释的用户画像
- 适合需要理解用户行为逻辑的研究者
部分可观测环境中的有效决策需要将长序列交互压缩为信息丰富的表示。我们提出描述性历史表示（DHRs）：通过回答关于过去交互和未来可能结果的问题来定义充分统计量。DHRs聚焦于捕捉任务相关查询所需的信息，为最优控制提供结构化的历史摘要。我们设计了一个多智能体学习框架，包含表示、决策与提问组件，采用联合目标优化，平衡奖励最大化与表示回答有意义问题的能力。该方法生成的表示能捕捉关键历史细节与预测结构，用于有效决策。我们在公开的电影与购物数据集上验证了该方法，生成可解释的文本用户画像，作为预测用户偏好行为的充分统计量。
原文摘要 · Abstract (English)
Effective decision making in partially observable environments requires compressing long interaction histories into informative representations. We introduce Descriptive History Representations (DHRs): sufficient statistics characterized by their capacity to answer relevant questions about past interactions and potential future outcomes. DHRs focus on capturing the information necessary to address task-relevant queries, providing a structured way to summarize a history for optimal control. We propose a multi-agent learning framework, involving representation, decision, and question-asking components, optimized using a joint objective that balances reward maximization with the representation's ability to answer informative questions. This yields representations that capture the salient historical details and predictive structures needed for effective decision making. We validate our approach on user modeling tasks with public movie and shopping datasets, generating interpretable textual user profiles which serve as sufficient statistics for predicting preference-driven behavior of users.
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