用图谱记忆用户行为，打造个性化的第一视角助手
EgoSelf: From Memory to Personalized Egocentric Assistant
- 构建基于图的交互记忆，捕捉用户行为的时间与语义关系
- 通过历史行为预测未来交互，提升个性化服务准确率
- 适合研究个性化智能助理与长期记忆建模的开发者
第一人称视角助手依赖第一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、偏好和日常规律，这种个性化对于真正有效的辅助至关重要。然而，如何有效整合长期用户数据进行个性化仍是一个关键挑战。为此，我们提出EgoSelf系统，包含基于图的交互记忆结构以及专门设计的个性化学习任务。该记忆从过往观察中构建，记录交互事件与实体间的时序和语义关联，并据此生成用户特定的画像。个性化学习任务被建模为一个预测问题：根据用户在图中记录的历史行为，预测其可能的未来交互。大量实验表明，EgoSelf作为个性化第一人称助手具有显著有效性。代码已公开于 https://abie-e.github.io/EgoSelf/。
原文摘要 · Abstract (English)
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/.
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