  Recommending appropriate items to users is crucial in many e-commerce
platforms that contain implicit data as users' browsing, purchasing and
streaming history. One common approach consists in selecting the N most
relevant items to each user, for a given N, which is called top-N
recommendation. To do so, recommender systems rely on various kinds of
information, like item and user features, past interest of users for items,
browsing history and trust between users. However, they often use only one or
two such pieces of information, which limits their performance. In this paper,
we design and implement GraFC2T2, a general graph-based framework to easily
combine and compare various kinds of side information for top-N recommendation.
It encodes content-based features, temporal and trust information into a
complex graph, and uses personalized PageRank on this graph to perform
recommendation. We conduct experiments on Epinions and Ciao datasets, and
compare obtained performances using F1-score, Hit ratio and MAP evaluation
metrics, to systems based on matrix factorization and deep learning. This shows
that our framework is convenient for such explorations, and that combining
different kinds of information indeed improves recommendation in general.
