DAReS is a proposed zero-configuration recommender system framework that uses a Dataset Description Language (DsDL) metadata schema to automate feature engineering, model selection, and hyperparameter tuning.
A trou- bling analysis of reproducibility and progress in recommen der systems research
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.IR 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Dataset-Agnostic Recommender Systems
DAReS is a proposed zero-configuration recommender system framework that uses a Dataset Description Language (DsDL) metadata schema to automate feature engineering, model selection, and hyperparameter tuning.