Modeling Attentive Interaction Behavior for Web Content Identification in Exploratory Information Seeking

Extracting and collecting information during web exploration is an arduous process, demanding substantial cognitive and physical effort from users. Users must not only determine which content is worth capturing but also manually extract and save it, often disrupting the flow of their learning and exploration. To mitigate it, we propose AIbM (Attentive Interaction Behavior Modeling), which encodes and integrates multiple implicit interaction data to identify fine-grained helpful web content, providing an algorithmic foundation to facilitate automatic information extraction and collection. AIbM captures dynamic patterns of user interactions---specifically eye and mouse movements---by encoding these into images and time series. It further employs graph modeling to exploit relationships between a content block and its adjacent blocks. By incorporating graph attention networks, AIbM efficiently processes both intra-block and inter-block features, improving the identification of helpful web content. Experimental results demonstrate that AIbM achieves the highest F1 score of 82.80% in general helpful content identification, marking improvements of 10.86% over state-of-the-art models like GBT and at least 2.49% over individual modality inputs. These results underscore the potential of our approach to advance tools and systems designed for automated information extraction and collection to facilitate more effective exploratory search experiences.