# BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale

arXiv: https://arxiv.org/abs/2410.02126 · HTML: https://mlsanity.com/paper/2410.02126/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [Semi-Implicit Variational Inference](https://mlsanity.com/paper/1805.11183/) (2018)
- [From Zero-Shot Learning to Cold-Start Recommendation](https://mlsanity.com/paper/1906.08511/) (2019)
- [Online Learning to Rank in Stochastic Click Models](https://mlsanity.com/paper/1703.02527/) (2017)
- [Learning Contextual Bandits in a Non-stationary Environment](https://mlsanity.com/paper/1805.09365/) (2018)
- [Differentiable Unbiased Online Learning to Rank](https://mlsanity.com/paper/1809.08415/) (2018)
- [Doubly Semi-Implicit Variational Inference](https://mlsanity.com/paper/1810.02789/) (2018)
- [Improving Deep Learning For Airbnb Search](https://mlsanity.com/paper/2002.05515/) (2020)
- [Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems](https://mlsanity.com/paper/2305.12102/) (2023)
- [Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model](https://mlsanity.com/paper/1905.12370/) (2019)

## Built on by (most cited first, 2 of 2)

- [Representation Curriculum: Stagewise Training for Robust Ranking and Allocation](https://mlsanity.com/paper/2606.09891/) (2026)
- [Fast Rates for Swap-Agnostic Learning of Proper Losses](https://mlsanity.com/paper/2607.28856/) (2026)

## Abstract (arXiv)

Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries. These models heavily rely on features derived from user interactions, such as clicks and engagement data. This dependence introduces cold start issues for items lacking user engagement and poses challenges in adapting to non-stationary shifts in user behavior over time. We address both challenges holistically as an online learning problem and propose BayesCNS, a Bayesian approach designed to handle cold start and non-stationary distribution shifts in search systems at scale. BayesCNS achieves this by estimating prior distributions for user-item interactions, which are continuously updated with new user interactions gathered online. This online learning procedure is guided by a ranker model, enabling efficient exploration of relevant items using contextual information provided by the ranker. We successfully deployed BayesCNS in a large-scale search system and demonstrated its efficacy through comprehensive offline and online experiments. Notably, an online A/B experiment showed a 10.60% increase in new item interactions and a 1.05% improvement in overall success metrics over the existing production baseline.