  Several researches on recommender systems are based on explicit rating data,
but in many real world e-commerce platforms, ratings are not always available,
and in those situations, recommender systems have to deal with implicit data
such as users' purchase history, browsing history and streaming history. In
this context, classical bipartite user-item graphs (BIP) are widely used to
compute top-N recommendations. However, these graphs have some limitations,
particularly in terms of taking temporal dynamic into account. This is not good
because users' preference change over time. To overcome this limit, the
Session-based Temporal Graph (STG) was proposed by Xiang et al. to combine
long- and short-term preferences in a graph-based recommender system. But in
the STG, time is divided into slices and therefore considered discontinuously.
This approach loses details of the real temporal dynamics of user actions. To
address this challenge, we propose the Link Stream Graph (LSG) which is an
extension of link stream representation proposed by Latapy et al. and which
allows to model interactions between users and items by considering time
continuously. Experiments conducted on four real world implicit datasets for
temporal recommendation, with 3 evaluation metrics, show that LSG is the best
in 9 out of 12 cases compared to BIP and STG which are the most used
state-of-the-art recommender graphs.
