# Neural Click Models for Recommender Systems

arXiv: https://arxiv.org/abs/2409.20055 · HTML: https://mlsanity.com/paper/2409.20055/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 23 of 23)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Reinforcement learning based recommender systems: A survey](https://mlsanity.com/paper/2101.06286/) (2021)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://mlsanity.com/paper/2105.06067/) (2021)
- [A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learning](https://mlsanity.com/paper/2110.01515/) (2021)
- [Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning](https://mlsanity.com/paper/1805.10000/) (2018)
- [RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising](https://mlsanity.com/paper/1808.00720/) (2018)
- [Horizon: Facebook's Open Source Applied Reinforcement Learning Platform](https://mlsanity.com/paper/1811.00260/) (2018)
- [Generative Adversarial Networks for Spatio-temporal Data: A Survey](https://mlsanity.com/paper/2008.08903/) (2020)
- [RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems](https://mlsanity.com/paper/2103.08057/) (2021)
- [Measuring Recommender System Effects with Simulated Users](https://mlsanity.com/paper/2101.04526/) (2021)
- [An Adversarial Imitation Click Model for Information Retrieval](https://mlsanity.com/paper/2104.06077/) (2021)
- [A Survey on Reinforcement Learning for Recommender Systems](https://mlsanity.com/paper/2109.10665/) (2021)
- [A Graph-Enhanced Click Model for Web Search](https://mlsanity.com/paper/2206.08621/) (2022)
- [A Click Sequence Model for Web Search](https://mlsanity.com/paper/1805.03411/) (2018)
- [ContentWise Impressions: An Industrial Dataset with Impressions Included](https://mlsanity.com/paper/2008.01212/) (2020)
- [RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System](https://mlsanity.com/paper/2110.11073/) (2021)
- [SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments](https://mlsanity.com/paper/2311.16586/) (2023)
- [Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments](https://mlsanity.com/paper/1301.2303/) (2013)
- [A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems](https://mlsanity.com/paper/2202.11351/) (2022)
- [All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation](https://mlsanity.com/paper/1909.00687/) (2019)

## Built on by (most cited first, 1 of 1)

- [LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering](https://mlsanity.com/paper/2411.00556/) (2024)

## Abstract (arXiv)

We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models. Proposed architectures include recurrent networks, Transformer-based models that alleviate the quadratic complexity of self-attention, adversarial and hierarchical architectures. Our models outperform baselines on the ContentWise and RL4RS datasets and can be used in RS simulators to model user response for RS evaluation and pretraining.