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

Armel Jacques NZEKON NZEKO'O, Maurice TCHUENTE & Matthieu LATAPY
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...
1 citation reported since publication in 2019.
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