A Comprehensive Survey on Biclustering-based Collaborative Filtering

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-06-22 DOI:10.1145/3674723
Miguel G. Silva, Sara C. Madeira, Rui Henriques
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Abstract

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.

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基于双聚类的协同过滤综合调查
协同过滤技术(CF)正处于一个高流行度的阶段。然而,用户偏好的多样性、用户-物品评分的结构稀疏性以及评分尺度固有的主观性都给推荐的成功带来了挑战。电子商务和电子娱乐平台不断增加的用户群和项目维度创造了机会,同时也进一步提高了对通用性和可扩展性的需求。为了应对这些挑战,基于用户和项目的 CF 聚类方法变得非常普遍。然而,传统的聚类方法评估的是所有项目(用户)的用户(项目)评级相似性,忽略了项目和用户配置文件的丰富多样性。相反,由于用户和物品子集的偏好通常同时相关,双聚类方法提供了一个自然的替代方案,近二十年来已成功应用于 CF 领域,并与新兴的深度学习 CF 立场协同整合。尽管如此,基于双聚类的 CF 原理并不统一,导致最先进的方法表现出明显的行为差异。这项工作提供了一个结构化的视角,说明双聚类方面如何影响推荐的成功率、覆盖率和效率。为此,我们引入了一种分类法来对这一领域的贡献进行分类,并全面考察了最先进的双聚类方法,突出了它们的局限性和潜力。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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