Collaborative filtering recommender systems taxonomy

In the era of internet access, recommender systems try to alleviate the difficulty that consumers face while trying to find items (e.g., services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of collaborative filtering recommend systems. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a collaborative filtering recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of collaborative filtering recommender systems according to their ability to efficiently handle well-known drawbacks.