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Titre: | Artificial Neural Networks for a Hybrid Recommendation System |
Auteur(s): | TAYEBI, Imen SMAILI, Hayet |
Mots-clés: | Collaborative Filtering, Content-based Filtering, Artificial neural networks, Recommender systems |
Date de publication: | 2020 |
Editeur: | Université Ibn Khaldoun -Tiaret- |
Résumé: | In the context of recommendation systems, metadata information from reviews written for businesses has rarely been considered in traditional systems developed using content-based and collaborative filtering approaches. Collaborative filtering and content-based filtering are popular memory-based methods for recommending new products to the users but suffer from some limitations and fail to provide effective recommendations in many situations. In this paper, we present a deep learning neural network framework that utilizes reviews in addition to contentbased features to generate model-based predictions for the business-user combinations. We show that a set of content and collaborative features allows for the development of a neural network model with the goal of minimizing the rating misclassification error using stochastic gradient descent optimization algorithm. We empirically show that the hybrid approach is a very promising solution when compared to standalone memory-based collaborative filtering method. |
URI/URL: | http://dspace.univ-tiaret.dz:80/handle/123456789/5383 |
Collection(s) : | Master |
Fichier(s) constituant ce document :
Fichier | Description | Taille | Format | |
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TH.M.INF.FR.2020.38.pdf | 2,27 MB | Adobe PDF | Voir/Ouvrir |
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