Research Article Open Access

ConvSVD++: A Hybrid Deep CF Recommender Model using Convolutional Neural Network

Mohamed Grida1, Lamiaa Fayed1 and Mohamed Hassan1
  • 1 Zagazig University, Egypt
Journal of Computer Science
Volume 16 No. 12, 2020, 1697-1708

DOI: https://doi.org/10.3844/jcssp.2020.1697.1708

Submitted On: 8 July 2020 Published On: 13 January 2021

How to Cite: Grida, M., Fayed, L. & Hassan, M. (2020). ConvSVD++: A Hybrid Deep CF Recommender Model using Convolutional Neural Network. Journal of Computer Science, 16(12), 1697-1708. https://doi.org/10.3844/jcssp.2020.1697.1708

Abstract

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.

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Keywords

  • Recommender System
  • Deep Learning
  • Prediction
  • Singular Value Decomposition
  • Convolution Neural Network