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A Survey of Recommender Systems Techniques, Challenges and Evaluation Metrics

Recommender systems are software applications that belong to a class of personalized information filtering technologies that aim to support decision making in large information space. There are various techniques being used to achieve this goal in traditional and mobile recommender systems. The recommender systems techniques are usually classified in four main categories: Collaborative Filtering (CF), Content Based Filtering (CBF), Knowledge Based Filtering (KBF) and Hybrid Filtering (HF). In this paper an overview of these techniques, challenges and evaluation metrics of recommender systems is discussed.