Summary of Towards Lifelong Learning Embeddings: An Algorithmic Approach to Dynamically Extend Embeddings, by Miguel Alves Gomes et al.
Towards Lifelong Learning Embeddings: An Algorithmic Approach to Dynamically Extend Embeddings
by Miguel Alves Gomes, Philipp Meisen, Tobias Meisen
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach for personalizing customer interactions in e-commerce settings is explored, leveraging deep learning models that rapidly recognize patterns in large datasets. These models use embeddings to map discrete information into a latent vector space, popularized in recent years. However, the dynamic nature of e-commerce, characterized by frequent new product introductions, poses challenges for these embeddings, which typically require fixed dimensions and inputs, necessitating periodic retraining from scratch. This paper proposes a modular algorithm that extends embedding input size while preserving learned knowledge, addressing e-commerce’s dynamism and cold start problems associated with new products. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary E-commerce companies want to know their customers better, but it’s hard when there are so many new products coming out all the time. Machine learning models can help by recognizing patterns in big datasets, which is great for personalizing customer interactions. The problem is that these models need fixed information and inputs, but e-commerce is always changing. This paper finds a way to make the models work better in this environment by extending their input size and keeping what they’ve learned. It also helps with new products that have never been seen before. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Machine learning » Vector space
