A Survey of Collaborative Filtering Techniques

As one of the most successful approaches to building recommender systems, collaborative filtering ( CF ) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their…

• FA
  Florida Atlantic University
• UD
  Universidad del Atlántico