# Deep neural network marketplace recommenders in online experiments

arXiv: https://arxiv.org/abs/1809.02130 · HTML: https://mlsanity.com/paper/1809.02130/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

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

- [Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling](https://mlsanity.com/paper/2104.15046/) (2021)
- [Efficient and Interpretable Bandit Algorithms](https://mlsanity.com/paper/2310.14751/) (2023)

## Abstract (arXiv)

Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper focuses on the challenge of measuring recommender performance and summarizes the online experiment results with several promising types of deep neural network recommenders - hybrid item representation models combining features from user engagement and content, sequence-based models, and multi-armed bandit models that optimize user engagement by re-ranking proposals from multiple submodels. The recommenders are currently running in production at the leading Norwegian marketplace FINN.no and serves over one million visitors everyday.