Domain Specific Data Distillation and Multi-modal Embedding Generation
This addresses the problem of limited domain-specific structured data for embedding generation, particularly in cloud computing, though it appears incremental as it builds on existing collaborative filtering methods.
The paper tackles the challenge of creating domain-centric embeddings by introducing a Hybrid Collaborative Filtering framework that leverages structured data to filter noise from unstructured data, resulting in embeddings that achieve a 28% lift in precision and an 11% lift in recall for domain-specific attribute prediction in cloud computing.
The challenge of creating domain-centric embeddings arises from the abundance of unstructured data and the scarcity of domain-specific structured data. Conventional embedding techniques often rely on either modality, limiting their applicability and efficacy. This paper introduces a novel modeling approach that leverages structured data to filter noise from unstructured data, resulting in embeddings with high precision and recall for domain-specific attribute prediction. The proposed model operates within a Hybrid Collaborative Filtering (HCF) framework, where generic entity representations are fine-tuned through relevant item prediction tasks. Our experiments, focusing on the cloud computing domain, demonstrate that HCF-based embeddings outperform AutoEncoder-based embeddings (using purely unstructured data), achieving a 28% lift in precision and an 11% lift in recall for domain-specific attribute prediction.