Economic recommendation based on pareto efficient resource allocation
Abstract: A fundamentally important role of the Web economy is Online Resource Allocation (ORA) from producers to consumers, such as product allocation in E-commerce, job allocation in freelancing platforms, and driver resource allocation in P2P riding services. Since users have the freedom to choose, such allocations are not provided in a forced manner, but usually in forms of personalized recommendation, where users have the right to refuse. Current recommendation approaches mostly provide allocations to match the preference of each individual user, instead of treating the Web application as a whole economic system where users therein are mutually correlated on the allocations. This lack of global view leads to Pareto inefficiency, i.e., we can actually improve the recommendations by bettering some users while not hurting the others, and it means that the system did not achieve its best possible allocation. This problem is especially severe when the total amount of each resource is limited
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource, 10 S.
- Sprache
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Englisch
- Anmerkungen
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Veröffentlichungsversion
begutachtet
- Erschienen in
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Discussion Papers / Wissenschaftszentrum Berlin für Sozialforschung, Forschungsschwerpunkt Markt und Entscheidung, Forschungsprofessur Market Design: Theory and Pragmatics ; Bd. SP II 2017-503
- Klassifikation
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Wirtschaft
- Ereignis
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Veröffentlichung
- (wo)
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Mannheim
- (wer)
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SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
- (wann)
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2017
- Ereignis
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Veröffentlichung
- (wo)
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Berlin
- (wer)
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Wissenschaftszentrum Berlin für Sozialforschung gGmbH
- (wann)
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2017
- Urheber
- Beteiligte Personen und Organisationen
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Wissenschaftszentrum Berlin für Sozialforschung gGmbH
- URN
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urn:nbn:de:101:1-2021101108494139336368
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:31 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Zhang, Yongfeng
- Zhang, Yi
- Friedman, Daniel
- Wissenschaftszentrum Berlin für Sozialforschung gGmbH
- SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
Entstanden
- 2017
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