Statistical Learning in Recommendation Systems: Personalized Ranking and Decentralized Learning Systems

Lee, James, Statistics - Graduate School of Arts and Sciences, University of Virginia
Tang, Xiwei, AS-Statistics (STAT), University of Virginia
Rodu, Jordan, AS-Statistics (STAT), University of Virginia
Wang, Lingxiao, AS-Statistics (STAT), University of Virginia
Yu, Shan, AS-Statistics (STAT), University of Virginia
Li, Sheng, DS-Faculty Affairs, University of Virginia

Recommendation systems are integral to various domains, including content-based platforms and e-commerce. Despite extensive research efforts in designing diverse recommendation systems in recent years, popular approaches based on explicit ratings often struggle to perform effectively in practical recommendation-making scenarios. This dissertation comprises three main sections. Firstly, we will present an overview of recommendation systems from a statistical perspective. Secondly, we propose a personalized ranking-based model that utilizes pairwise preference information from explicit feedback, demonstrating superior performance compared to conventional approaches in real-world settings. Finally, addressing growing concerns over privacy and data safety within recommendation systems, we introduce a novel decentralized federated learning framework. This framework operates without relying on centralized data aggregation or costly model merging procedures.

PHD (Doctor of Philosophy)
Recommender Systems, Federated Learning, Informative Missing
Issued Date: