Ranking In Generalized Linear Bandits

• URL: http://arxiv.org/abs/2207.00109v2
• Date: Mon, 1 Jan 2024 21:27:44 GMT
• Title: Ranking In Generalized Linear Bandits
• Authors: Amitis Shidani, George Deligiannidis, Arnaud Doucet
• Abstract summary: We study the ranking problem in generalized linear bandits. In recommendation systems, displaying an ordered list of the most attractive items is not always optimal.
• Score: 38.567816347428774
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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