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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

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The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Recommender Systems and Techniques
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

849 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
849 works in the cohort · of 4,299,418page 5 of 17

Labels cover 1 of 849 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 849 of 849 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

affunlabeled
TopRecs
Mohammad Khabbaz, Laks V. S. Lakshmanan
2011· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Meta-Learning for Online Update of Recommender Systems
Minseok Kim, Hwanjun Song, Yooju Shin, Dongmin Park, Kijung Shin, Jae-Gil Lee
2022· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
distilled prediction:candidate · noneconsensus · none
19
citations
affunlabeled
A Contrastive Sharing Model for Multi-Task Recommendation
Ting Bai, Yu‐Dong Xiao, Bin Wu, Guojun Yang, Hongyong Yu, Jian‐Yun Nie
2022· article· en· Proceedings of the ACM Web Conference 2022· Computer Science
distilled prediction:candidate · noneconsensus · none
17
citations
affunlabeled
The keepup recommender system
Andrew Webster, Julita Vassileva
2007· article· en· Computer Science
distilled prediction:candidate · noneconsensus · none
16
citations
affno abstractunlabeled
Predicting Users’ Future Interests on Twitter
Fattane Zarrinkalam, Hossein Fani, Ebrahim Bagheri, Mohsen Kahani
2017· book-chapter· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrow+scholarly_communication+open_scienceconsensus · none
15
citations
afffundno abstractunlabeled
Composite recommendations: from items to packages
Min Xie, Laks V. S. Lakshmanan, Peter T. Wood
2012· article· en· Frontiers of Computer Science· Computer Science
distilled prediction:candidate · noneconsensus · none
15
citations
affno abstractunlabeled
User Modeling 2001
Mathias Bauer, Julita Vassileva, Piotr J. Gmytrasiewicz
2001· book· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrow+open_scienceconsensus · none
15
citations
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