  Recommender systems have been widely applied to assist user's decision making
by providing a list of personalized item recommendations. Context-aware
recommender systems (CARS) additionally take context information into
considering in the recommendation process, since user's tastes on the items may
vary from contexts to contexts. Several context-aware recommendation algorithms
have been proposed and developed to improve the quality of recommendations.
However, there are limited research which explore and discuss the capability of
interpreting the contextual effects by the recommendation models. In this
paper, we specifically focus on different contextual modeling approaches,
reshape the structure of the models, and exploit how to utilize the existing
contextual modeling to interpret the contextual effects in the recommender
systems. We compare the explanations of contextual effects, as well as the
recommendation performance over two-real world data sets in order to examine
the quality of interpretations.
