  Recommender systems help users to find their appropriate items among large
volumes of information. Different types of recommender systems have been
proposed. Among these, context-aware recommender systems aim at personalizing
as much as possible the recommendations based on the context situation in which
the user is. In this paper we present an approach integrating contextual
information into the recommendation process by modeling either item-based or
user-based influence of the context on ratings, using the Pearson Correlation
Coefficient. The proposed solution aims at taking advantage of content and
contextual information in the recommendation process. We evaluate and show
effectiveness of our approach on three different contextual datasets and
analyze the performances of the variants of our approach based on the
characteristics of these datasets, especially the sparsity level of the input
data and amount of available information.
