Artificial intelligence in recommender systems
- PDF / 591,793 Bytes
- 19 Pages / 595.276 x 790.866 pts Page_size
- 92 Downloads / 280 Views
POSITION PAPER
Artificial intelligence in recommender systems Qian Zhang1 · Jie Lu1
· Yaochu Jin2
Received: 26 June 2020 / Accepted: 28 September 2020 © The Author(s) 2020
Abstract Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI. Keywords Recommender systems · Artificial intelligence · Computational intelligence
Introduction It is challenging for businesses in a competitive marketplace to offer products and services that appeal directly to an individual customer’s needs. Personalized e-services help to solve a major problem—that of information overload—thereby making the decision process easier for customers and enhancing user experience. The recommender systems used in these personalized e-services were first established twenty years ago and were developed by employing techniques and theories drawn from other artificial intelligence (AI) fields for user profiling and preference discovery. The past few years have seen a huge increase in success-
B
Jie Lu [email protected] Qian Zhang [email protected] Yaochu Jin [email protected]
1
Decision Systems and e-Service Intelligence Laboratory, Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia
2
Department of Computer Science, University of Surrey, Guildford, Surrey GU27XH, UK
ful AI-driven applications. Successes include Deepmind’s AlphaGo, the AI-driven program that famously won the game ‘Go’ against a professional human player, and the selfdriving car, as well as others in the areas of computer vision and speech recognition. These continuing advances in AI, data analytics and big data present a great opportunity for recommender systems to embrace the impressive achievements of AI. Various AI techniques have more recently been applied to rec
Data Loading...
