• 1 Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]
• 2 Université de Yaoundé I

Recommending suitable products to users is crucial in e-commerce and streaming platforms. In some situations, a customer has a preference for a product based on the product features and the current temporal context. It is therefore wise to take these aspects into account in order to improve the quality of the recommendations. In this paper, we propose recommender systems based on the availability prediction of product categories according to the temporal context. Indeed, the classification of the Top-N recommendations proposed by the initial recommender system is updated in such a way as to favor products with categories predicted available. Furthermore, we propose an algorithm for the choice of the appropriate temporal context to consider for the availability prediction of categories. Experiments are carried out on four datasets and comparisons are made on the results of three basic recommender systems with and without integration of availability forecasts, according to the Hit-ratio, MAP and F1-score evaluation metrics. We note that in 75% of cases, to have the best performance, it is necessary to integrate the availabilities prediction of the categories. This gain can even go to more than 12% regardless of the dataset. All this confirms the relevance of our contribution.