# Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty

arXiv: https://arxiv.org/abs/2006.10932 · HTML: https://mlsanity.com/paper/2006.10932/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2020.

## Built on by (most cited first, 11 of 11)

- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Uncertainty Quantification and Decomposition for LLM-based Recommendation](https://mlsanity.com/paper/2501.17630/) (2025)
- [Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation](https://mlsanity.com/paper/2209.11679/) (2022)
- [Fashion-Specific Attributes Interpretation via Dual Gaussian Visual-Semantic Embedding](https://mlsanity.com/paper/2210.17417/) (2022)
- [HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot Filling](https://mlsanity.com/paper/2310.09135/) (2023)
- [Unified Uncertainty Estimation for Cognitive Diagnosis Models](https://mlsanity.com/paper/2403.14676/) (2024)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer](https://mlsanity.com/paper/2306.14314/) (2023)
- [Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation](https://mlsanity.com/paper/2307.13468/) (2023)
- [Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Space](https://mlsanity.com/paper/2504.10005/) (2025)
- [Are Recommenders Self-Aware? Label-Free Recommendation Performance Estimation via Model Uncertainty](https://mlsanity.com/paper/2507.23208/) (2025)

## Abstract (arXiv)

Most of existing embedding based recommendation models use embeddings (vectors) corresponding to a single fixed point in low-dimensional space, to represent users and items. Such embeddings fail to precisely represent the users/items with uncertainty often observed in recommender systems. Addressing this problem, we propose a unified deep recommendation framework employing Gaussian embeddings, which are proven adaptive to uncertain preferences exhibited by some users, resulting in better user representations and recommendation performance. Furthermore, our framework adopts Monte-Carlo sampling and convolutional neural networks to compute the correlation between the objective user and the candidate item, based on which precise recommendations are achieved. Our extensive experiments on two benchmark datasets not only justify that our proposed Gaussian embeddings capture the uncertainty of users very well, but also demonstrate its superior performance over the state-of-the-art recommendation models.