Effective and Privacy-preserving Federated Online Learning to Rank
Shuyi Wang, Bing Liu, Shengyao Zhuang, Guido Zuccon · 2021
Online Learning to Rank (OLTR) has been primarily studied in the centralised setting, where a central server is responsible to index the searchable data, collect the users' queries and search interactions, and optimize ranking models. A drawback of such a centralised OLTR paradigm is that it cannot guarantee user's privacy as all data (both the searchable one and the one related to user interactions) is collected by the server.