TY  - JOUR
AU  - Grida, Mohamed 
AU  - Fayed, Lamiaa 
AU  - Hassan, Mohamed 
PY  - 2021
TI  - ConvSVD++: A Hybrid Deep CF Recommender Model using Convolutional Neural Network
JF  - Journal of Computer Science
VL  - 16
IS  - 12
DO  - 10.3844/jcssp.2020.1697.1708
UR  - https://thescipub.com/abstract/jcssp.2020.1697.1708
AB  - Recommender systems are powerful systems that give added value to business and corporation. They are relatively recent technology and they will only keep improving in the future. The most widely used algorithms for recommender systems are categorized into the traditional recommender and deep-based recommender system. The traditional recommendation algorithm suffers from sparse data that significantly degrades recommendation accuracy. The hybrid approaches are attempts to tackle recommendation challenges. This paper addresses the integration of deep learning into traditional recommendation approaches especially, Collaborative Filtering (CF) algorithms to get a significant accurate prediction. It proposes a hybrid deep CF recommender model called ConvSVD++ that tightly integrates Convolution Neural Network (CNN) and Singular Value Decomposition (SVD++). The proposed model incorporates items’ content, implicit user’s feedback along with explicit item-user interaction to enhance prediction accuracy and handle sparsity problem. The proposed model is evaluated and all baseline models based on Movielens- 1M datasets. The results are evaluated using Root Mean Squared Error (RMSE) metric and it is concluded that the proposed model ConvSVD++ outperforms the baselines models. Accordingly, it is concluded that integrating CNN with SVD++ algorithm improves rating prediction accuracy.