# Adaptively Learning to Select-Rank in Online Platforms

arXiv: https://arxiv.org/abs/2406.05017 · HTML: https://mlsanity.com/paper/2406.05017/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [An Introduction to Matrix Concentration Inequalities](https://mlsanity.com/paper/1501.01571/) (2015)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Cascading Bandits: Learning to Rank in the Cascade Model](https://mlsanity.com/paper/1502.02763/) (2015)
- [MNL-Bandit: A Dynamic Learning Approach to Assortment Selection](https://mlsanity.com/paper/1706.03880/) (2017)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Seq2Slate: Re-ranking and Slate Optimization with RNNs](https://mlsanity.com/paper/1810.02019/) (2018)
- [TopRank: A practical algorithm for online stochastic ranking](https://mlsanity.com/paper/1806.02248/) (2018)
- [Multiple-Play Bandits in the Position-Based Model](https://mlsanity.com/paper/1606.02448/) (2016)
- [Learning a Product Relevance Model from Click-Through Data in E-Commerce](https://mlsanity.com/paper/2102.07098/) (2021)
- [Online Learning to Rank with Features](https://mlsanity.com/paper/1810.02567/) (2018)
- [DCM Bandits: Learning to Rank with Multiple Clicks](https://mlsanity.com/paper/1602.03146/) (2016)
- [A Study of Position Bias in Digital Library Recommender Systems](https://mlsanity.com/paper/1802.06565/) (2018)
- [Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://mlsanity.com/paper/1902.00245/) (2019)
- [Improving Micro-video Recommendation by Controlling Position Bias](https://mlsanity.com/paper/2208.05315/) (2022)
- [A General Theory of the Stochastic Linear Bandit and Its Applications](https://mlsanity.com/paper/2002.05152/) (2020)
- [Ranking In Generalized Linear Bandits](https://mlsanity.com/paper/2207.00109/) (2022)

## Abstract (arXiv)

Ranking algorithms are fundamental to various online platforms across e-commerce sites to content streaming services. Our research addresses the challenge of adaptively ranking items from a candidate pool for heterogeneous users, a key component in personalizing user experience. We develop a user response model that considers diverse user preferences and the varying effects of item positions, aiming to optimize overall user satisfaction with the ranked list. We frame this problem within a contextual bandits framework, with each ranked list as an action. Our approach incorporates an upper confidence bound to adjust predicted user satisfaction scores and selects the ranking action that maximizes these adjusted scores, efficiently solved via maximum weight imperfect matching. We demonstrate that our algorithm achieves a cumulative regret bound of O(d√NKT) for ranking K out of N items in a d-dimensional context space over T rounds, under the assumption that user responses follow a generalized linear model. This regret alleviates dependence on the ambient action space, whose cardinality grows exponentially with N and K (thus rendering direct application of existing adaptive learning algorithms -- such as UCB or Thompson sampling -- infeasible). Experiments conducted on both simulated and real-world datasets demonstrate our algorithm outperforms the baseline.