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.

翻译：在本篇技术综述中，我们全面总结了推荐系统领域的最新进展。本研究旨在概述该领域当前的最新技术水平，并强调推荐系统发展的最新趋势。研究首先对推荐系统的主要分类进行了全面总结，包括个性化推荐系统和群组推荐系统，进而深入探讨了基于知识的推荐系统类别。此外，本文还分析了推荐系统中的鲁棒性、数据偏差及公平性问题，总结了用于评估这些系统性能的指标。最后，本研究提供了对推荐系统发展最新趋势的见解，并指出了该领域未来研究的新方向。