The ACM Lifelog Search Challenge (LSC) is a venue that welcomes and compares systems that support the exploration of lifelog data, and in particular the retrieval of specific information, through an interactive competition format. This paper reviews the recent advances in interactive lifelog retrieval as demonstrated at the ACM LSC from 2022 to 2024. Through a detailed comparative analysis, we highlight key improvements across three main retrieval tasks: known-item search, question answering, and ad-hoc search. Our analysis identifies trends such as the widespread adoption of embedding-based retrieval methods (e.g., CLIP, BLIP), increased integration of large language models (LLMs) for conversational retrieval, and continued innovation in multimodal and collaborative search interfaces. We further discuss how specific retrieval techniques and user interface (UI) designs have impacted system performance, emphasizing the importance of balancing retrieval complexity with usability. Our findings indicate that embedding-driven approaches combined with LLMs show promise for lifelog retrieval systems. Likewise, improving UI design can enhance usability and efficiency. Additionally, we recommend reconsidering multi-instance system evaluations within the expert track to better manage variability in user familiarity and configuration effectiveness.

翻译：ACM生命日志搜索挑战赛（LSC）是一个通过互动竞赛形式，汇集并比较支持生命日志数据探索（尤其是特定信息检索）系统的平台。本文回顾了2022年至2024年ACM LSC中展示的交互式生命日志检索技术的最新进展。通过详细的对比分析，我们重点阐述了在已知项搜索、问答系统及即席搜索三大核心检索任务中的关键改进。分析揭示了以下趋势：基于嵌入的检索方法（如CLIP、BLIP）的广泛采用、大型语言模型（LLM）在对话式检索中融合度的提升，以及多模态与协同搜索界面的持续创新。我们进一步探讨了特定检索技术与用户界面（UI）设计如何影响系统性能，并强调在检索复杂度与可用性之间保持平衡的重要性。研究结果表明，嵌入驱动方法与LLM的结合为生命日志检索系统带来了发展前景；同时，优化UI设计能够有效提升可用性与效率。此外，我们建议在专家赛道中重新考量多实例系统评估机制，以更好地应对用户熟悉度差异与配置有效性波动带来的影响。