Skip to Main content Skip to Navigation
Journal articles

Système de recommandation avec prise en compte de la prévision de disponibilité des catégories de produits

Abstract : 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.
Document type :
Journal articles
Complete list of metadata
Contributor : Armel Jacques Nzekon Nzeko’o Connect in order to contact the contributor
Submitted on : Wednesday, June 1, 2022 - 1:05:03 PM
Last modification on : Thursday, June 9, 2022 - 3:10:21 AM


Files produced by the author(s)



Armel Jacques Nzekon Nzeko’o, Hamza Adamou, Maurice Tchuente. Système de recommandation avec prise en compte de la prévision de disponibilité des catégories de produits. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2022, Volume 36 - Special issue CRI 2021, Volume 36 - Special issue CRI 2021 (36), ⟨10.46298/arima.9156⟩. ⟨hal-03591997v2⟩



Record views


Files downloads