Auteurs: | » Sarah Medjroud » DENNOUNI Nassim » HADJ HENNI MHAMED » Djelloul Bettache |
Type : | Conférence Internationale |
Nom de la conférence : | First International Conference on Big Data, IoT, Web Intelligence and Applications (BIWA) |
Lieu : Sidi Bel Abbès | Pays: ALGERIA |
Lien : » https://www.proceedings.com/67631.html | |
Publié le : | 11-12-2022 |
Nowadays, Recommender Systems (RSs) have become the indispensable solution to the problem of information overload in many different fields (e-commerce, e-tourism, ...) because they offer their customers with more adapted and increasingly personalized services. In this context, collaborative filtering (CF) techniques are used by many RSs since they make it easier to provide recommendations of acceptable quality by leveraging the preferences of similar user communities. However, these types of techniques suffer from the problem of the sparsity of user evaluations, especially during the cold start phase. Indeed, the process of searching for similar neighbors may not be successful due to insufficient data in the matrix of user-item ratings (case of a new user or new item). To solve this kind of problem, we can find in the literature several solutions which allow to overcome the insufficiency of the data thanks to the …