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Recommending appropriate items to users is crucial in many e-commerceplatforms that contain implicit data as users' browsing, purchasing andstreaming history. One common approach consists in selecting the N mostrelevant items to each user, for a given N, which is called top-Nrecommendation. To do so, recommender systems rely on various kinds ofinformation, like item and user features, past interest of users for items,browsing history and trust between users. However, they often use only one ortwo such pieces of information, which limits their performance. In this paper,we design and implement GraFC2T2, a general graph-based framework to easilycombine and compare various kinds of side information for top-N recommendation.It encodes content-based features, temporal and trust information into acomplex graph, and uses personalized PageRank on this graph to performrecommendation. We conduct experiments on Epinions and Ciao datasets, andcompare obtained performances using F1-score, Hit ratio and MAP evaluationmetrics, to systems based on matrix factorization and deep learning. This showsthat our framework is convenient for such explorations, and that combiningdifferent kinds of information indeed improves recommendation in general.

A general graph-based framework for top-N recommendation using content, temporal and trust information