Optimizing Ranking Systems Online as Bandits

TL;DR

This paper addresses the challenges of optimizing online ranking systems using bandit algorithms, proposing novel methods to improve effectiveness, safety, adaptability to user preference changes, and result diversification.

Contribution

It introduces new algorithms for online ranking optimization tackling effectiveness, safety, nonstationarity, and diversification, advancing the application of bandit frameworks in real-world systems.

Findings

MergeDTS effectively evaluates online rankers.

BubbleRank ensures safe content display.

CascadeDUCB and CascadeSWUCB handle nonstationary user preferences.

Abstract

Ranking system is the core part of modern retrieval and recommender systems, where the goal is to rank candidate items given user contexts. Optimizing ranking systems online means that the deployed system can serve user requests, e.g., queries in the web search, and optimize the ranking policy by learning from user interactions, e.g., clicks. Bandit is a general online learning framework and can be used in our optimization task. However, due to the unique features of ranking, there are several challenges in designing bandit algorithms for ranking system optimization. In this dissertation, we study and propose solutions for four challenges in optimizing ranking systems online: effectiveness, safety, nonstationarity, and diversification. First, the effectiveness is related to how fast the algorithm learns from interactions. We study the effective online ranker evaluation task and propose the MergeDTS algorithm to solve the problem effectively. Second, the deployed algorithm should be safe, which means the algorithm only displays reasonable content to user requests. To solve the safe online learning to rank problem, we propose the BubbleRank algorithm. Third, as users change their preferences constantly, the algorithm should handle the nonstationarity. We formulate this nonstationary online learning to rank problem as cascade non-stationary bandits and propose CascadeDUCB and CascadeSWUCB algorithms to solve the problem. Finally, the contents in ranked lists should be diverse. We consider the results diversification task and propose the CascadeHybird algorithm that considers both the item relevance and results diversification when learning from user interactions.