Ranking In Generalized Linear Bandits
AAAI 2024 Workshop on Recommendation Ecosystems: Modeling, Optimization and Incentive Design 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 ordere...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
30.06.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | AAAI 2024 Workshop on Recommendation Ecosystems: Modeling,
Optimization and Incentive Design 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. |
---|---|
DOI: | 10.48550/arxiv.2207.00109 |