III: Medium: Collaborative Research: Self-Supervised Recommender System Learning with Application Specific Adaption

在大数据时代，为了有效地帮助人们获得他们想要的信息，推荐系统被各种在线平台广泛采用。推荐系统旨在为用户提供高质量的推荐服务。除了电子商务，其他潜在的应用还包括精准医疗以推荐针对性的患者治疗、在线社交网络中的朋友推荐、决策支持、电子学习等，然而，各种数据质量问题和模型学习挑战将为推荐系统在真实的世界中的部署造成巨大障碍。为了应对这些挑战，该项目探索开发新的技术来学习推荐系统，这些系统不依赖于人工标签或注释等监督信息，这些信息的获取成本很高。这被称为推荐系统自监督学习，它提供了一种有前途的学习范式，可以从数据本身发现监督信号，而不需要昂贵的手动注释。作为一种有效的技术，自监督学习将使推荐系统能够在各种具有挑战性的应用场景中很好地工作，为人们提供几乎所有现有在线平台的高质量和公平的推荐服务。本计画的主要目的是发展一个具有自我监督学习的推荐系统架构，并探讨其各种延伸，为推荐系统学习发展统一且可延伸的原则、方法与技术，并研究推荐系统自我监督学习的一般适用性与效益。本项目研究的推荐系统任务极具挑战性，原因有很多：（1）缺乏监督信息，这使得许多现有的推荐模型无效;（2）固有的数据偏差，这可能导致对少数用户群体的不公平对待;（3）冷启动问题，这涉及到对收集到的信息很少的主题的推理问题;以及（4）推荐系统动态性，其反映用户的变化特征或行为。本计画将利用一个新颖且可扩充的图类神经网路模型，来解决推荐系统在学习表示上的挑战。基于最先进的自监督学习技术，例如，数据增强旨在显著增加可用于训练模型的数据的多样性而不实际收集新数据，以及对比学习旨在学习简洁的数据表示，使得相似的样本保持彼此接近，而不相似的样本远离，所提出的模型可以用自监督学习进行预训练，这将通过有效的微调进一步转移到解决本项目中研究的问题。具体而言，该项目将重点研究四个主要任务：（1）面向公平性的推荐系统预训练和微调，（2）通过数据增强的冷启动推荐系统学习;（3）跨平台推荐系统对比学习;（4）通过自监督模型调整的终身动态推荐系统学习。就更广泛的影响而言，除了本项目中调查的推荐任务外，这些研究的进展具有变革潜力，可以在改革当前和未来的人工智能模型公平性，可信度，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

In the era of big data, to effectively help people get their desired information, recommender systems are widely adopted by various online platforms. Recommender systems aim to provide users with high-quality recommendation services. In addition to e-commerce, other potential applications include precision medicine to recommend targeted patient treatment, friend recommendation in online social networks, decision support, e-learning, etc. However, various data quality problems and model learning challenges will create great obstacles for recommender system deployments in the real world. To address these challenges, this project explores to develop new techniques for learning recommender systems that don’t rely on supervision information like manual label or annotation, which can be costly to obtain. This is referred to as the recommender system self-supervised learning, which provides a promising learning paradigm that can discover the supervision signals from the data itself without the need of costly manual annotation. As an effective technique, self-supervised learning will enable recommender systems to work well in a variety of challenging application scenarios to provide people with high-quality and fair recommendation services for almost all the existing online platforms mentioned above. This project focuses on developing a general recommender system framework with self-supervised learning, and investigating its various extensions.This project will develop unified and extensible principles, methods, and technologies for recommender system learning, and study the general applicability and benefit of recommender system self-supervised learning. The recommender system tasks studied in this project are extremely challenging due to many reasons: (1) lack of supervision information, which renders many existing recommendation models to be ineffective; (2) inherent data biases, which can lead to unfair treatment to the minority user groups; (3) the cold-start problem, which concerns on the issue of inferences for subjects with little collected information; and (4) recommender system dynamics, which reflects the changing characteristics or behaviors of the users. This project will these challenges on learning representations for recommender systems with a novel and extensible graph neural network model. Based on the state-of-the-art self-supervised learning techniques, e.g., data augmentation which aims to significantly increase the diversity of data available for training models without actually collecting new data, and contrastive learning which intends to learn succinct data representations such that similar samples stay close to each other, while dissimilar ones are far apart, the proposed model can be pre-trained with self-supervised learning, which will be further transferred to address the problems studied in this project via effective fine-tuning. Specifically, this project will focus on studying four main tasks: (1) fairness-oriented recommender systems pre-training and fine-tuning, (2) cold-start recommender system learning via data augmentation; (3) inter-platform recommender system contrastive learning; and (4) lifelong dynamic recommender system learning via self-supervised model tuning. In terms of broader impacts, besides the recommendation tasks as investigated in this project, advances in such research studies have transformative potentials for fundamental development in reforming the current and future AI model fairness, trustworthiness, and lifelong learning studies in broad applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.