Learning to Rank in the Position Based Model with Bandit Feedback

• URL: http://arxiv.org/abs/2004.13106v1
• Date: Mon, 27 Apr 2020 19:12:20 GMT
• Title: Learning to Rank in the Position Based Model with Bandit Feedback
• Authors: Beyza Ermis, Patrick Ernst, Yannik Stein, Giovanni Zappella
• Abstract summary: We propose novel extensions of two well-known algorithms viz. LinUCB and Linear Thompson Sampling to the ranking use-case. To account for the biases in a production environment, we employ the position-based click model.
• Score: 3.9121134770873742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalization is a crucial aspect of many online experiences. In particular, content ranking is often a key component in delivering sophisticated personalization results. Commonly, supervised learning-to-rank methods are applied, which suffer from bias introduced during data collection by production systems in charge of producing the ranking. To compensate for this problem, we leverage contextual multi-armed bandits. We propose novel extensions of two well-known algorithms viz. LinUCB and Linear Thompson Sampling to the ranking use-case. To account for the biases in a production environment, we employ the position-based click model. Finally, we show the validity of the proposed algorithms by conducting extensive offline experiments on synthetic datasets as well as customer facing online A/B experiments.

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