Recent Developments and Limitations in Recommender Systems: A Review
Tharinda Dilshan Piyadasa, Ravidu Suien Rammuni Silva, Dharshana Kasthurirathna · 2022
Recommender systems are an indispensable and inseparable part of everyday online content offerings like music, movies, sports, and e-commerce. They help users by personalizing products, services, and content, cutting the time and effort required to browse through large amounts of online information. Despite the evolution of recommender systems, the extent to which they can be explored and extended is yet to be discovered. This paper conducts an extensive review of recent developments in recommender systems and presents a comprehensive analysis, highlighting their approaches and limitations. Based on the findings of the analysis, recommendations are provided on possible research avenues that look promising to investigate further. The primary objective of this study is to provide an entry point for researchers who intend to address the current limitations of recommender systems and develop new strategies. Based on the recent breakthroughs in recommender systems detailed in this study, it is possible to find various prospective research avenues, not limited to novel recommender systems but also new evaluation mechanisms and security measures.