Armel Jacques Nzekon Nzeko’o, Maurice Tchuente and Matthieu Latapy
Journal of Interdisciplinary Methodologies and Issues in Sciences, 2019
Recommending appropriate items to users is crucial in many e-commerce platforms that containimplicit data as users’ browsing, purchasing and streaming history. One common approach con-sists in selecting the N most relevant items to each user, for a given N, which is called top-Nrecommendation. To do so, recommender systems rely on various kinds of information, like itemand user features, past interest of users for items, browsing history and trust between users. How-ever, 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 easilycombine and compare various kinds of side information for top-N recommendation. It encodescontent-based features, temporal and trust information into a complex graph, and uses personal-ized PageRank on this graph to perform recommendation. We conduct experiments on Epinionsand Ciao datasets, and compare obtained performances using F1-score, Hit ratio and MAP eval-uation metrics, to systems based on matrix factorization and deep learning. This shows that ourframework is convenient for such explorations, and that combining different kinds of informationindeed improves recommendation in general.