  Most existing approaches in Context-Aware Recommender Systems (CRS) focus on
recommending relevant items to users taking into account contextual
information, such as time, location, or social aspects. However, few of them
have considered the problem of user's content dynamicity. We introduce in this
paper an algorithm that tackles the user's content dynamicity by modeling the
CRS as a contextual bandit algorithm and by including a situation clustering
algorithm to improve the precision of the CRS. Within a deliberately designed
offline simulation framework, we conduct evaluations with real online event log
data. The experimental results and detailed analysis reveal several important
discoveries in context aware recommender system.
