Federated Unbiased Learning to Rank

by   Chang Li, et al.

Unbiased Learning to Rank (ULTR) studies the problem of learning a ranking function based on biased user interactions. In this framework, ULTR algorithms have to rely on a large amount of user data that are collected, stored, and aggregated by central servers. In this paper, we consider an on-device search setting, where users search against their personal corpora on their local devices, and the goal is to learn a ranking function from biased user interactions. Due to privacy constraints, users' queries, personal documents, results lists, and raw interaction data will not leave their devices, and ULTR has to be carried out via Federated Learning (FL). Directly applying existing ULTR algorithms on users' devices could suffer from insufficient training data due to the limited amount of local interactions. To address this problem, we propose the FedIPS algorithm, which learns from user interactions on-device under the coordination of a central server and uses click propensities to remove the position bias in user interactions. Our evaluation of FedIPS on the Yahoo and Istella datasets shows that FedIPS is robust over a range of position biases.


page 1

page 2

page 3

page 4


An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems

Federated online learning to rank (FOLTR) aims to preserve user privacy ...

Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings

The classical machine learning paradigm requires the aggregation of user...

Handling Position Bias for Unbiased Learning to Rank in Hotels Search

Nowadays, search ranking and recommendation systems rely on a lot of dat...

A Frequency-Based Learning-To-Rank Approach for Personal Digital Traces

Personal digital traces are constantly produced by connected devices, in...

Reinforcement Online Learning to Rank with Unbiased Reward Shaping

Online learning to rank (OLTR) aims to learn a ranker directly from impl...

Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift

With privacy as a motivation, Federated Learning (FL) is an increasingly...

Model-based Unbiased Learning to Rank

Unbiased Learning to Rank (ULTR) that learns to rank documents with bias...

Please sign up or login with your details

Forgot password? Click here to reset