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Two-level monotonic multistage recommender systems

Electronic Journal of Statistics(2021)10.1214/21-ejs1924Source: DataRank Database

Two-level monotonic multistage recommender systems is a research paper published in Electronic Journal of Statistics (2021). On theSindex it has a DataRank of 0.

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Two-level monotonic multistage recommender systems · arXiv

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (6 grants)Impact (FWCI 0.00)OA: goldTopics (36)Linked Datasets (1)DataCite Reuse (1)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

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Reusable (0/3)

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Authors (3)

Xiaotong Shen ORCID,Wei Pan ORCID,Ben Dai ORCID

Live enrichment

PubMed:20725694 OpenAlex:https://openalex.org/W3205032321

National Institutes of Health

Grant: 5R01GM126002-04

Estimation and Inference of Gene Regulatory Networks

National Institutes of Health

Grant: 5R01HL105397-06

Genetic Association and Personalized Medicine

National Science Foundation

Grant: 1952539

FRG: Collaborative Research: Generative Learning on Unstructured Data with Applications to Natural Language Processing and Hyperlink Prediction

National Institutes of Health

Grant: 1R01AG069895-01

Deep Learning with Neuroimaging Genetic Data for Alzheimer's Disease

National Science Foundation

Grant: 1712564

Collaborative Research: Collaborative Learning for Multimodal Data

National Science Foundation

Grant: 1721216

Collaborative Research: Automatic Video Interpretation and Description

FWCI

0.00

Citation Percentile

0.2%

goldLicense: cc-by

journalhttps://projecteuclid.org/journals/electronic-journal-of-statistics/volume-15/issue-2/Two-level-monotonic-multistage-recommender-systems/10.1214/21-EJS1924.pdf

publisherhttps://projecteuclid.org/journals/electronic-journal-of-statistics/volume-15/issue-2/Two-level-monotonic-multistage-recommender-systems/10.1214/21-EJS1924.pdf

journalhttps://doi.org/10.1214/21-ejs1924

repositoryhttp://arxiv.org/abs/2110.06116

repositoryhttps://arxiv.org/pdf/2110.06116.pdf

Fields of Study

Recommender Systems and TechniquesData Stream Mining TechniquesMachine Learning and Data Classification02 engineering and technology01 natural sciences0202 electrical engineering, electronic engineering, information engineering0101 mathematics

Keywords

Monotonic functionComputer scienceRecommender systemCategorical variableLeverage (statistics)Property (philosophy)Machine learningCollaborative filteringArtificial intelligenceConsistency (knowledge bases)Margin (machine learning)Data miningMathematicsFOS: Computer and information sciencesComputer Science - Machine LearningClassification and discrimination; cluster analysis (statistical aspects)Learning and adaptive systems in artificial intelligencenonconvex minimizationMathematics - Statistics TheoryMachine Learning (stat.ML)Statistics Theory (math.ST)monotonicityComputer Science - Information RetrievalMachine Learning (cs.LG)Statistics - Machine LearningFOS: Mathematicsimplicit feedbacknonnegative latent factor modelInformation Retrieval (cs.IR)

Sustainable Development Goals

SDG 0: Peace, Justice and strong institutions

Two-level monotonic multistage recommender systems

10.48550/arxiv.2110.06116arXiv