Ranking In Generalized Linear Bandits

TL;DR

This paper introduces algorithms for ranking in generalized linear bandits, accounting for position and item dependencies, with applications in recommendation systems, and extends existing models to more complex scenarios.

Contribution

It develops UCB and Thompson Sampling algorithms for ranking with position and item dependencies, generalizing previous models and linking the problem to graph theory.

Findings

Algorithms effectively handle position and item dependencies.

Generalizes existing ranking models to complex scenarios.

Connects ranking problem to graph theory for broader analysis.

Abstract

We study the ranking problem in generalized linear bandits. At each time, the learning agent selects an ordered list of items and observes stochastic outcomes. In recommendation systems, displaying an ordered list of the most attractive items is not always optimal as both position and item dependencies result in a complex reward function. A very naive example is the lack of diversity when all the most attractive items are from the same category. We model the position and item dependencies in the ordered list and design UCB and Thompson Sampling type algorithms for this problem. Our work generalizes existing studies in several directions, including position dependencies where position discount is a particular case, and connecting the ranking problem to graph theory.