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想要的信息，推荐系统被各种网络平台广泛采用。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，以推荐有针对性的患者治疗，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现实世界中的部署带来巨大障碍。为了应对这些挑战，该项目探索开发新的学习推荐系统技术，这些技术不依赖于手动标签或注释等监督信息，这些信息的获取成本很高。这被称为推荐系统的自监督学习，它提供了一种很有前途的学习范式，可以从数据本身中发现监督信号，而不需要昂贵的人工注释。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，为人们提供高质量和公平的推荐服务，几乎适用于上述所有现有的在线平台。这个项目的重点是开发一个具有自我监督学习的通用推荐系统框架，并研究其各种扩展。本项目将开发统一的、可扩展的推荐系统学习原理、方法和技术，研究推荐系统自监督学习的一般适用性和效益。本项目所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，使得现有的许多推荐模型无效；(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（cold-start problem），即在被试收集的信息很少的情况下进行推理的问题；(4)推荐系统的动态性，它反映了用户的变化特征或行为。该项目将利用一种新颖的、可扩展的图神经网络模型来学习推荐系统的表示。基于最先进的自监督学习技术，例如数据增强技术，目的是在不实际收集新数据的情况下显著增加可用于训练模型的数据的多样性；对比学习技术，旨在学习简洁的数据表示，使相似的样本彼此接近，而不相似的样本相隔很远，本文提出的模型可以通过自监督学习进行预训练。这将进一步转移，通过有效的微调来解决本项目研究的问题。具体而言，本项目将重点研究四个主要任务：(1)面向公平的推荐系统预训练和微调；(2)通过数据增强冷启动推荐系统学习；(3)跨平台推荐系统对比学习；(4)基于自监督模型调优的终身动态推荐系统学习。就更广泛的影响而言，除了本项目所研究的推荐任务外，此类研究的进展在改革当前和未来人工智能模型的公平性、可信度以及广泛应用中的终身学习研究方面具有根本性的变革潜力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.