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