Ouhbi, B. and Frikh, B. and Zemmouri, E. and Abbad, A. (2018) Deep Learning Based Recommender Systems. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Recommender Systems (RSs) are valuable and practical tools that help users to find interesting products in a large space of possible options. Many hybrid recommender systems combine collaborative filtering and content-based approach to build a more robust system. This paper aims to propose a new deep learning based recommender system to enhance recommendation performance and to overcome the limitations of existing approaches, especially when dealing with the cold start problem. So, a hybrid model based on Deep Belief Networks and item-based collaborative filtering is proposed. We conducted experiments on MovieLens 100K dataset. The results showed that our method outperforms existing hybrid recommender systems. © 2018 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Big data; Collaborative filtering; Recommender systems, Cold start problems; Content-based approach; Deep belief networks; Hybrid model; Hybrid recommender systems; Item-based collaborative filtering; Recommendation performance; Robust systems, Deep learning
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:45
URI: http://eprints.umi.ac.ma/id/eprint/2323

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