推荐系统中的机器遗忘：一项洞见
Machine Unlearning for Recommendation Systems: An Insight
AI总结：
本文综述推荐系统中的机器遗忘（MUL），分析其在适应性、个性化、隐私、偏见与算法透明度方面的应用、挑战及方法局限，并指出未来研究方向。
摘要：
本综述探讨推荐系统中的机器遗忘（MUL），以应对适应性、个性化、隐私和偏见挑战。与传统模型不同，MUL根据用户偏好变化和伦理考量动态调整系统知识。本文批判性地考察了MUL的基础、现实应用以及算法透明度等挑战。文章梳理相关文献，就MUL如何变革推荐提出洞见，讨论用户信任，并为负责任、以用户为中心的人工智能（AI）未来研究提出路径。本文引导研究人员理解个性化与隐私权衡相关挑战，鼓励作出贡献以满足定向数据删除的实际需求。文章强调MUL在安全、自适应机器学习中的作用，并提出拓展其边界的方法。本文的新颖之处在于探讨了这些方法的局限性，从而凸显推动该领域发展的令人振奋的前景。
英文摘要：
This review explores machine unlearning (MUL) in recommendation systems, addressing adaptability, personalization, privacy, and bias challenges. Unlike traditional models, MUL dynamically adjusts system knowledge based on shifts in user preferences and ethical considerations. The paper critically examines MUL's basics, real-world applications, and challenges like algorithmic transparency. It sifts through literature, offering insights into how MUL could transform recommendations, discussing user trust, and suggesting paths for future research in responsible and user-focused artificial intelligence (AI). The document guides researchers through challenges involving the trade-off between personalization and privacy, encouraging contributions to meet practical demands for targeted data removal. Emphasizing MUL's role in secure and adaptive machine learning, the paper proposes ways to push its boundaries. The novelty of this paper lies in its exploration of the limitations of the methods, which highlights exciting prospects for advancing the field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
