SEMCo uses sparse entmax contrastive learning for purely content-based cold-start item recommendation, outperforming standard methods in ranking accuracy.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
DDAP is a controlled agentic framework that guides non-experts via four LLM-assisted stages to construct competitive AI pipelines for business, biology, and health domains.
citing papers explorer
-
Sparse Contrastive Learning for Content-Based Cold Item Recommendation
SEMCo uses sparse entmax contrastive learning for purely content-based cold-start item recommendation, outperforming standard methods in ranking accuracy.
-
From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists
DDAP is a controlled agentic framework that guides non-experts via four LLM-assisted stages to construct competitive AI pipelines for business, biology, and health domains.