Summary of Domain Specific Data Distillation and Multi-modal Embedding Generation, by Sharadind Peddiraju et al.
Domain Specific Data Distillation and Multi-modal Embedding Generation
by Sharadind Peddiraju, Srini Rajagopal
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed modeling approach addresses the challenge of creating domain-centric embeddings by leveraging structured data to filter noise from unstructured data. The novel technique, based on Hybrid Collaborative Filtering (HCF), fine-tunes generic entity representations through relevant item prediction tasks, resulting in high-precision and high-recall embeddings for domain-specific attribute prediction. In experiments focusing on the cloud computing domain, HCF-based embeddings outperform AutoEncoder-based embeddings by 28% in precision and 11% in recall. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to create special embeddings that are tailored to specific domains, like cloud computing. The method uses both structured and unstructured data together to reduce noise and improve the accuracy of these domain-specific embeddings. This is important for predicting certain attributes within a specific domain, where HCF-based embeddings perform better than those generated by AutoEncoders. |
Keywords
» Artificial intelligence » Autoencoder » Precision » Recall
