EAGLE: Eyegaze-Assisted Guidance and Learning Evaluation for Lifeloging Retrieval

In this work, we focus on bridging the gap between how expert users and novice users use lifelog information retrieval systems. Our proposed system aims to achieve two main objectives: 1) incorporating implicit interactions such as eye movements into the search process, and 2) using an automated search flow to support novice and experienced users based on a minimal effort principle. Based on user interactions with the search system, we propose an algorithm that rearranges and modifies how information is displayed and organized based on user eye movements, which aims to optimize display results. Our results on previous LSC topics show that leveraging user input data in the search process is beneficial and profitable that future human-centric systems should exploit for improved performance.