A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks

Abstract

In the era of big data, recommender system (RS) has become an effective information filtering tool that alleviates information overload for Web users. Collaborative filtering (CF), as one of the most successful recommendation techniques, has been widely studied by various research institutions and industries and has been applied in practice. CF makes recommendations for the current active user using lots of users' historical rating information without analyzing the content of the information resource. However, in recent years, data sparsity and high dimensionality brought by big data have negatively affected the efficiency of the traditional CF-based recommendation approaches. In CF, the context information, such as time information and trust relationships among the friends, is introduced into RS to construct a training model to further improve the recommendation accuracy and user's satisfaction, and therefore, a variety of hybrid CF-based recommendation algorithms have emerged. In this paper, we mainly review and summarize the traditional CF-based approaches and techniques used in RS and study some recent hybrid CF-based recommendation approaches and techniques, including the latest hybrid memory-based and model-based CF recommendation algorithms. Finally, we discuss the potential impact that may improve the RS and future direction. In this paper, we aim at introducing the recent hybrid CF-based recommendation techniques fusing social networks to solve data sparsity and high dimensionality and provide a novel point of view to improve the performance of RS, thereby presenting a useful resource in the state-of-the-art research result for future researchers.

Keywords

• Recommender systems
• collaborative filtering
• matrix factorization
• singular value decomposition
• trust-aware collaborative filtering
• social networks