Recommendation of optimized information seeking process based on the similarity of user access behavior patterns

Differing from many studies of recommendation that provided the final results directly, our study focuses on providing an optimized process of information seeking to users. Based on process mining, we propose an integrated adaptive framework to support and facilitate individualized recommendation based on the gradual adaptation model that gradually adapts to a target user's transition of needs and behaviors of information access, including various search-related activities, over different time spans. In detail, successful information seeking processes are extracted from the information seeking histories of users. Furthermore, these successful information seeking processes are optimized as a series of action units to support the target users whose information access behavior patterns are similar to the reference users. Based on these, the optimized information seeking processes are navigated to the target users according to their transitions of interest focus. In addition to describing some definitions and measures introduced, we go further to present an optimized process recommendation model and show the system architecture. Finally, we discuss the simulation and scenario for the proposed system.