Collaborative Filtering (CF) is achieving a plateau of high popularity. Still, recommendation success is challenged by the diversity of user preferences, structural sparsity of user-item ratings, and inherent subjectivity of rating scales. The increasing user base and item dimensionality of e-commerce and e-entertainment platforms creates opportunities, while further raising generalization and scalability needs. Moved by the need to answer these challenges, user-based and item-based clustering approaches for CF became pervasive. However, classic clustering approaches assess user (item) rating similarity across all items (users), neglecting the rich diversity of item and user profiles. Instead, as preferences are generally simultaneously correlated on subsets of users and items, biclustering approaches provide a natural alternative, being successfully applied to CF for nearly two decades and synergistically integrated with emerging deep learning CF stances. Notwithstanding, biclustering-based CF principles are dispersed, causing state-of-the-art approaches to show accentuated behavioral differences. This work offers a structured view on how biclustering aspects impact recommendation success, coverage, and efficiency. To this end, we introduce a taxonomy to categorize contributions in this field and comprehensively survey state-of-the-art biclustering approaches to CF, highlighting their limitations and potentialities.

协同过滤 (CF) 正在达到高度普及的平台期。尽管如此，推荐的成功仍然受到用户偏好的多样性、用户项目评分的结构稀疏性以及评分量表固有的主观性的挑战。电子商务和电子娱乐平台不断增长的用户群和商品维度创造了机会，同时进一步提高了通用性和可扩展性需求。出于应对这些挑战的需要，基于用户和基于项目的 CF 聚类方法变得普遍。然而，经典的聚类方法评估所有项目（用户）之间的用户（项目）评分相似性，忽略了项目和用户配置文件的丰富多样性。相反，由于偏好通常同时与用户和项目子集相关，双聚类方法提供了一种自然的替代方案，已成功应用于 CF 近二十年，并与新兴的深度学习 CF 立场协同集成。尽管如此，基于双聚类的 CF 原理是分散的，导致最先进的方法表现出明显的行为差异。这项工作提供了关于双聚类方面如何影响推荐成功、覆盖范围和效率的结构化视图。为此，我们引入了一种分类法来对该领域的贡献进行分类，并全面调查最先进的 CF 双聚类方法，强调它们的局限性和潜力。