Techniques that are used to filter the data in order to make the data compatible to the standard RS model which includes the three main concepts user-items-ratings. Collaborative Filtering, Content Based Filtering and Hybrid filtering are the most known techniques. The selection of a filtering technique depends on the type of data which will be filtered.
Published in Chapter:
Recommender Systems Review of Types, Techniques, and Applications
George A. Sielis (University of Cyprus, Cyprus), Aimilia Tzanavari (University of Nicosia, Cyprus & Cyprus University of Technology, Cyprus), and George A. Papadopoulos (University of Cyprus, Cyprus)
Copyright: © 2015
|Pages: 11
DOI: 10.4018/978-1-4666-5888-2.ch714
Abstract
Recommender or recommendation systems are software tools that make useful suggestions to users, by taking into account their profile, preferences and/or actions during interaction with an application or website. They are usually personalized and can refer to items to buy, people to connect to or books/ articles to read. Recommender Systems (RS) aim at helping users with their interaction by bringing to surface the information that is relevant to them, their needs, or their tasks. This article's objective is to present a review of the different types of RS, the techniques and methods used for building such systems, the algorithms used to generate the recommendations and how these systems can be evaluated. Finally, a number of topics are discussed as envisioned future research directions.