Self-supervised learning (SSL) has gained significant interest in recent years as a solution to address the challenges posed by sparse and noisy data in recommender systems. Despite the growing number of SSL algorithms designed to provide state-of-the-art performance in various recommendation scenarios (e.g., graph collaborative filtering, sequential recommendation, social recommendation, KG-enhanced recommendation), there is still a lack of unified frameworks that integrate recommendation algorithms across different domains. Such a framework could serve as the cornerstone for self-supervised recommendation algorithms, unifying the validation of existing methods and driving the design of new ones. To address this gap, we introduce SSLRec, a novel benchmark platform that provides a standardized, flexible, and comprehensive framework for evaluating various SSL-enhanced recommenders. The SSLRec library features a modular architecture that allows users to easily evaluate state-of-the-art models and a complete set of data augmentation and self-supervised toolkits to help create SSL recommendation models with specific needs. Furthermore, SSLRec simplifies the process of training and evaluating different recommendation models with consistent and fair settings. Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field. Our implemented SSLRec framework is available at the source code repository https://github.com/HKUDS/SSLRec.

翻译：自监督学习（SSL）近年来作为应对推荐系统中稀疏与噪声数据挑战的解决方案，已获得广泛关注。尽管针对不同推荐场景（如图协同过滤、序列推荐、社交推荐、知识图谱增强推荐）涌现了大量旨在实现最优性能的SSL算法，但当前仍缺乏能够整合跨领域推荐算法的统一框架。此类框架可作为自监督推荐算法的基石，统一现有方法的验证并推动新算法的设计。为填补这一空白，我们提出SSLRec——一个新型基准平台，为评估各类SSL增强推荐器提供了标准化、灵活且全面的框架。SSLRec库采用模块化架构，使用户能够便捷评估最先进模型，同时包含完整的数据增强与自监督工具包，支持按需定制SSL推荐模型。此外，SSLRec在一致公平的设置下简化了不同推荐模型的训练与评估流程。该平台覆盖了跨场景的全套最新SSL增强推荐模型，使研究人员能够评估这些前沿模型并推动领域创新。实现的SSLRec框架已开源至代码仓库 https://github.com/HKUDS/SSLRec。