# EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding

arXiv: https://arxiv.org/abs/2308.01329 · HTML: https://mlsanity.com/paper/2308.01329/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 2 Aug 2023.

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

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [From Word to Sense Embeddings: A Survey on Vector Representations of Meaning](https://mlsanity.com/paper/1805.04032/) (2018)
- [Embedding Projector: Interactive Visualization and Interpretation of Embeddings](https://mlsanity.com/paper/1611.05469/) (2016)
- [Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples](https://mlsanity.com/paper/1912.04853/) (2019)
- [EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection](https://mlsanity.com/paper/1808.09074/) (2018)
- [embComp: Visual Interactive Comparison of Vector Embeddings](https://mlsanity.com/paper/1911.01542/) (2019)
- [Merchant Category Identification Using Credit Card Transactions](https://mlsanity.com/paper/2011.02602/) (2020)
- [Visually Analyzing Contextualized Embeddings](https://mlsanity.com/paper/2009.02554/) (2020)
- [Towards a Flexible Embedding Learning Framework](https://mlsanity.com/paper/2009.10989/) (2020)
- [Learning Item-Interaction Embeddings for User Recommendations](https://mlsanity.com/paper/1812.04407/) (2018)
- [Constrained Non-Affine Alignment of Embeddings](https://mlsanity.com/paper/1910.05862/) (2019)

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

- [TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding](https://mlsanity.com/paper/2511.19693/) (2025)
- [Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach](https://mlsanity.com/paper/2401.02987/) (2024)

## Abstract (arXiv)

Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance reported from different embedding learning algorithms, few efforts were devoted to structurally interpreting how features are encoded in the learned embedding space. This work proposes EmbeddingTree, a hierarchical embedding exploration algorithm that relates the semantics of entity features with the less-interpretable embedding vectors. An interactive visualization tool is also developed based on EmbeddingTree to explore high-dimensional embeddings. The tool helps users discover nuance features of data entities, perform feature denoising/injecting in embedding training, and generate embeddings for unseen entities. We demonstrate the efficacy of EmbeddingTree and our visualization tool through embeddings generated for industry-scale merchant data and the public 30Music listening/playlists dataset.