Recommandation mobile, sensible au contexte de contenus évolutifs: Contextuel-E-Greedy
This addresses mobile content recommendation for users with evolving preferences, though it appears incremental as it builds on existing exploration-exploitation tradeoff methods.
The paper tackles the problem of dynamic user content recommendation by introducing Contextuel-E-Greedy, an algorithm that adaptively balances exploration and exploitation based on context, and demonstrates it outperforms surveyed algorithms in experiments.
We introduce in this paper an algorithm named Contextuel-E-Greedy that tackles the dynamicity of the user's content. It is based on dynamic exploration/exploitation tradeoff and can adaptively balance the two aspects by deciding which situation is most relevant for exploration or exploitation. The experimental results demonstrate that our algorithm outperforms surveyed algorithms.