A survey of collaborative filtering-based systems for online recommendation
Dorin Militaru, Costin Zaharia · 2010
Internet services operate on a vastly larger scale and permit virtual interactions. The Internet and Web has created vast new opportunities, providing an infrastructure that enables buyers and sellers to find each other online. Companies can now offer many products, services and information easily and with lower costs. It becomes more and more difficult for customers to find quickly what they are looking for. Nevertheless, recommendation systems are playing a major role. Collaborative filtering (CF), or recommender system based-CF, has appeared as one methodology designed to perform such a recommendation task. These systems allow people to use expressed preferences of thousands of other people in order to find the product they desire based on the level of similarity between tastes. The concept has appeared from convergent research on search browsers, intelligent agents and data mining, and it allows to avoid the difficult question of "why" consumers prefer this or that product or brand.