While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.

中文翻译：

---

非 IID 推荐系统：推荐范式转换的回顾和框架

虽然推荐在我们的生活、学习、工作和娱乐中扮演着越来越重要的角色，但我们收到的推荐往往是针对不相关、重复或无趣的产品和服务。这种糟糕推荐的一个关键原因在于现有理论和系统中推荐的用户和项目是独立且同分布的 (IID) 的内在假设。另一个现象是，虽然已经做出了巨大努力来对用户或项目的特定方面进行建模，但整体用户和项目特征及其非 IID 性却被忽视了。本文讨论了推荐的非 IID 性质和特点，然后建立了非 IID 理论框架，以建立对推荐问题内在本质的深入和全面的理解，从耦合和异质性的角度来看。这种非 IID 推荐研究触发了从 IID 到非 IID 推荐研究的范式转变，并有望提供知情、相关、个性化和可操作的建议。它创建了令人兴奋的新方向和基本解决方案，以解决各种复杂性，包括冷启动、基于稀疏数据、跨域、基于组和先令攻击相关的问题。