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Record W2290243676 · doi:10.1016/j.ifacol.2015.06.416

Matching Service Providers and Customers in Two-Sided Dynamic Markets

2015· article· en· W2290243676 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIFAC-PapersOnLine · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGame Theory and Voting Systems
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMatching (statistics)Consistency (knowledge bases)Computer scienceDisjoint setsBlossom algorithmOptimal matching3-dimensional matchingService (business)Service providerDistributed computingAlgorithmData miningMathematical optimizationArtificial intelligenceMathematicsBusinessMarketing

Abstract

fetched live from OpenAlex

This paper presents matching algorithms for two-sided dynamic service markets where service providers and customers form two disjoint sets and an agent from one side of the market can be matched only with an agent from the other side. We address the challenges derived from dynamic changes of the market. The algorithms are designed based on re-matching and repair-based matching models. The re-matching algorithm is straightforward and easy to implement. However, it does not have a mechanism to maintain matching consistency with the previous matching solution. Instead of computing a completely new matching solution, the repair-based matching algorithm maintain good matching consistency by repairing only the part of matching affected by the dynamic changes. In addition to better matching consistency, we show that the matching solutions generated by the repair-based matching algorithm are also stable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.259
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it