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
this https URL

自我监督学习在推荐系统中解决数据稀疏和噪声的挑战中引起了广泛关注。但目前缺乏统一框架来集成不同领域的推荐算法，为此我们引入了 SSLRec，一个基准平台，为评估各种 SSL 增强的推荐器提供了标准化、灵活和综合的框架，并简化了不同推荐模型的训练和评估过程。