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.
Highlights
The Recommender System (RS) has become a significant web service in many applications
Work considered traditional recommendation approaches that are classified into Collaborative Filtering (CF), Content-Based (CB) and hybrid approach
We propose a hybrid deep Collaborative Filtering (CF) model called ConvSVD++ that incorporates items’ content, implicit user’s feedback along with explicit user-item interactions to enhance rating prediction accuracy and handle sparsity problem
Summary
The Recommender System (RS) has become a significant web service in many applications. A deep recommender system can be developed in accordance with collaborative or content-based models It can be developed basically upon the DL algorithm or integrated tightly or loosely with other traditional RS models (Zhang et al, 2019). The hybrid deep recommendation approach is one that integrates traditional and deep-based models as an attempt to overcome traditional approaches limitations and to provide an accurate recommendation Various articles applied these models to incorporate various auxiliary information to improve accuracy and solve sparsity and cold start problems. We propose a hybrid deep Collaborative Filtering (CF) model called ConvSVD++ that incorporates items’ content, implicit user’s feedback along with explicit user-item interactions to enhance rating prediction accuracy and handle sparsity problem. A proposed model tightly integrates a Convolutional Neural Network (CNN) and the SVD++ approach.
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