Recent Developments in Recommender Systems: A Survey
It provides an overview for researchers and practitioners, but is incremental as it compiles existing knowledge without introducing new methods.
This survey summarizes the latest advancements in recommender systems, covering taxonomy, knowledge-based approaches, and issues like robustness and fairness, while highlighting current trends and future research directions.
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.