Literature Review of Recommendation Systems

Author:

Gironacci Irene Maria1

Affiliation:

1. Swinburne University of Technology, Australia

Abstract

Artificial intelligence technologies are currently at the core of many sectors and industries—from cyber security to healthcare—and also have the power to influence the governance of domestic industry, the security and privacy citizens. In particular, the rise of new machine learning methods, such as those used in recommendation systems, provides many opportunities in terms of personalization. Big players like YouTube, Amazon, Netflix, Spotify, and many others are currently using recommendation systems to improve their business. Recommender systems are critical in some industries as they can generate income and provide a way to stand out from competitors. In this chapter, a literature review of recommendation systems is presented, as well as the application of recommendation systems in industry.

Publisher

IGI Global

Reference58 articles.

1. Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications

2. A SURVEY OF RECOMMENDER SYSTEM TYPES AND ITS CLASSIFICATION

3. Recommender Systems in Industry: A Netflix Case Study

4. ifWeb: a Prototype of User Model-based Intelligent Agent for Documentation Filtering and Navigation in the Word Wide Web.;F.Asnicar;Proceedings of the First International Workshop on Adaptive Systems and User Modeling on the World Wide Web, Sixth International Conference on User Modeling,1997

5. Fab: Content-based, collaborative recommendation.;M.Balabanovic;Commun Assoc Comput Mach,1997

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