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FINALLY: A Dataset Recommender System for Recommender-Systems Research — arXiv:2609.08941

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08941
License (as archived): CC0-1.0
Archived: 2026-10-16T15:05:27.287Z
SHA-256 of the archived text: 936c7365084262211082a357d656644be1ae3c2e0363d7e6cdcefcc826aec58a
Stable URL of this page: https://metraipolis.com/documento/dc_616201ff7bad

How to cite

FINALLY: A Dataset Recommender System for Recommender-Systems Research — arXiv:2609.08941 — arXiv (en), https://arxiv.org/abs/2609.08941 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_616201ff7bad (Metraipolis archive dc_616201ff7bad, sha256 936c7365084262211082a357d656644be1ae3c2e0363d7e6cdcefcc826aec58a)

BibTeX

@misc{dc616201ff7bad,
  title = {FINALLY: A Dataset Recommender System for Recommender-Systems Research — arXiv:2609.08941},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08941},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_616201ff7bad; sha256 936c7365084262211082a357d656644be1ae3c2e0363d7e6cdcefcc826aec58a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08941. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Dataset selection shapes the empirical conditions under which recommender-system algorithms are evaluated, yet existing tools provide limited support for constructing complete dataset sets that jointly satisfy experimental constraints and set-level selection objectives. To address this problem, I developed FINALLY, a web-based dataset recommender for constructing configurable dataset sets for offline recommender-systems evaluations. FINALLY combines required datasets, candidate-pool restrictions, metadata filters, configurable target-set sizes, Random selection, and diverse and non-diverse strategies based on adapted Effective Covariance and Convex Hull objectives. I evaluated FINALLY through 420 recommendation runs across ten systematically varied configurations. All evaluated dataset sets satisfied the applicable target-size, duplicate-avoidance, snapshot-membership, required-dataset, and metadata-filter requirements. All 40 deterministic strategy--configuration combinations were reproducible. Both the Effective-Covariance-based and Convex-Hull-based strategies produced the expected diverse-versus-non-diverse score ordering in all ten configurations. Under their corresponding objectives, the diverse strategies produced scores above all 30 configuration-specific Random results, whereas the non-diverse strategies produced scores below all 30 Random results. These results establish technical consistency for the evaluated FINALLY workflow and show that the implemented strategies follow their intended optimization directions within the investigated configuration space. They do not establish the scientific suitability, global optimality, or practical superiority of the generated selections.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in cs.IR).
Categorie arXiv: Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2609.08941 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T14:49:33.969Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).