EgoSelf：从记忆到个性化第一人称助手
EgoSelf: From Memory to Personalized Egocentric Assistant
AI总结：
EgoSelf通过构建基于图的交互记忆和个性化学习任务，实现个性化第一人称助手，有效整合长期用户数据以提升服务效果。
摘要：
第一人称助手通常依赖第一人称视角数据来捕捉用户行为和上下文以提供个性化服务。由于不同用户表现出不同的习惯、偏好和例行公事，这种个性化对于真正有效的帮助至关重要。然而，有效整合长期用户数据以实现个性化仍是一个关键挑战。为此，我们引入了EgoSelf系统，该系统包含一个基于图的交互记忆，该记忆由过去观察构建而成，并包含一个专门的个性化学习任务。记忆捕捉了交互事件和实体之间的时间和语义关系，从中推导出用户特定的档案。个性化学习任务被形式化为一个预测问题，其中模型根据图中记录的个体用户的历史行为预测可能的未来交互。广泛的实验证明了EgoSelf作为个性化第一人称助手的有效性。代码可在https://abie-e.github.io/EgoSelf/上获得。
英文摘要：
Egocentric assistants often rely on first-person view data to capture user behavior and context for personalized services. Since different users exhibit distinct habits, preferences, and routines, such personalization is essential for truly effective assistance. However, effectively integrating long-term user data for personalization remains a key challenge. To address this, we introduce EgoSelf, a system that includes a graph-based interaction memory constructed from past observations and a dedicated learning task for personalization. The memory captures temporal and semantic relationships among interaction events and entities, from which user-specific profiles are derived. The personalized learning task is formulated as a prediction problem where the model predicts possible future interactions from individual user's historical behavior recorded in the graph. Extensive experiments demonstrate the effectiveness of EgoSelf as a personalized egocentric assistant. Code is available at https://abie-e.github.io/EgoSelf/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
