# Towards a Flexible Embedding Learning Framework

arXiv: https://arxiv.org/abs/2009.10989 · HTML: https://mlsanity.com/paper/2009.10989/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph](https://mlsanity.com/paper/2208.05648/) (2022)
- [VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations](https://mlsanity.com/paper/2104.02797/) (2021)
- [Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks](https://mlsanity.com/paper/2109.10020/) (2021)
- [EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding](https://mlsanity.com/paper/2308.01329/) (2023)

## Abstract (arXiv)

Representation learning is a fundamental building block for analyzing entities in a database. While the existing embedding learning methods are effective in various data mining problems, their applicability is often limited because these methods have pre-determined assumptions on the type of semantics captured by the learned embeddings, and the assumptions may not well align with specific downstream tasks. In this work, we propose an embedding learning framework that 1) uses an input format that is agnostic to input data type, 2) is flexible in terms of the relationships that can be embedded into the learned representations, and 3) provides an intuitive pathway to incorporate domain knowledge into the embedding learning process. Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database. Moreover, a sampling mechanism is carefully designed to establish a direct connection between the input and the information captured by the output embeddings. To complete the representation learning toolbox, we also outline a simple yet effective post-processing technique to properly visualize the learned embeddings. Our empirical results demonstrate that the proposed framework, in conjunction with a set of relevant entity-relation-matrices, outperforms the existing state-of-the-art approaches in various data mining tasks.