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.

这篇技术综述全面总结了推荐系统领域的最新进展，提供了该领域现状的概述并强调了推荐系统发展的最新趋势，涵盖了个性化、群组、基于知识的推荐系统等主要分类，探讨了推荐系统中的鲁棒性、数据偏见和公平性问题，并总结了评估指标用于评估这些系统的性能，最后还深入分析了推荐系统的最新趋势和未来研究的新方向。

推荐系统的近期发展：一份综述

Recent Developments in Recommender Systems: A Survey

We show that if a (locally compact) group $G$ acts properly on a locally
compact $\sigma$-compact space $X$ then there is a family of $G$-invariant
proper continuous finite-valued pseudometrics which induces the topology of
$X$. If $X$ is furthermore metrizable then $G$ acts properly on $X$ if and only
if there exists a $G$-invariant proper compatible metric on $X$.

若群 G 在本地紧点 X 上适当地作用，则有一族 G 不变的连续有限值 pseudometric 可诱导 X 的拓扑；如果 X 进一步是可度量的，则当且仅当 X 上存在 G 不变的适当兼容度量时，G 在 X 上适当地作用。