Abstract
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 ability to address the challenges. From basic techniques
to the state-of-the-art, we attempt to present a comprehensive survey
for CF techniques, which can be served as a roadmap for research
and practice in this area.
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