Autoencoder-based General Purpose Representation Learning for Customer Embedding

arXiv:2402.18164v1 Announce Type: new
Abstract: In recent years, exploiting the domain-specific underlying structure of data and its generative factors for representation learning has shown success in various use-case agnostic applications. However, the diversity and complexity of tabular data have made it challenging to represent these structures in a latent space through multi-dimensional vectors. We design an autoencoder-based framework for building general purpose embeddings, we assess the performance of different autoencoder architectures, and show simpler models outperform complex ones in embedding …

abstract applications arxiv autoencoder case complexity cs.ai cs.lg customer data diversity domain embedding general generative representation representation learning space success tabular tabular data through type