In this technical survey, we comprehensively summarize the latest advancements in the field of recommender systems. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. The study starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems, and then delves into the category of knowledge-based recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, the study provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field.
@article{arxiv.2306.12680,
title = {Recent Developments in Recommender Systems: A Survey},
author = {Yang Li and Kangbo Liu and Ranjan Satapathy and Suhang Wang and Erik Cambria},
journal= {arXiv preprint arXiv:2306.12680},
year = {2023}
}