research-article

'MASTerful' Matchmaking in Service Transactions: Inferred Abilities, Needs and Interests versus Activity Histories

Authors:

Victoria Bellotti,

Afsaneh Doryab,

Dean Leitersdorf,

Benjamin V. Hanrahan,

John M. CarrollAuthors Info & Claims

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Pages 1644 - 1655

https://doi.org/10.1145/2858036.2858263

Published: 07 May 2016 Publication History

Abstract

Timebanking is a growing type of peer-to-peer service exchange, but is hampered by the effort of finding good transaction partners. We seek to reduce this effort by using a Matching Algorithm for Service Transactions (MAST). MAST matches transaction partners in terms of similarity of interests and complementarity of abilities and needs. We present an experiment involving data and participants from a real timebanking network, that evaluates the acceptability of MAST, and shows that such an algorithm can retrieve matches that are subjectively better than matches based on matching the category of people's historical offers or requests to the category of a current transaction request.

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Index Terms

'MASTerful' Matchmaking in Service Transactions: Inferred Abilities, Needs and Interests versus Activity Histories
1. Human-centered computing

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cover image ACM Conferences

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

May 2016

6108 pages

ISBN:9781450333627

DOI:10.1145/2858036

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Yahoo
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Microsoft
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University of Iowa

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Published: 07 May 2016

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CHI '16 Paper Acceptance Rate 565 of 2,435 submissions, 23%;

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https://dl.acm.org/doi/10.1145/3479599
Jun JSeo WPark JPark SJung H(2021)Exploring the Experiences of Streamers with Visual ImpairmentsProceedings of the ACM on Human-Computer Interaction10.1145/34760385:CSCW2(1-23)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3476038
Palomares IPorcel CPizzato LGuy IHerrera-Viedma E(2021)Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendationInformation Fusion10.1016/j.inffus.2020.12.00169(103-127)Online publication date: May-2021
https://doi.org/10.1016/j.inffus.2020.12.001
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https://doi.org/10.4018/IJMHCI.2019010103
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Chen JDoryab AHanrahan BYousfi ABeck JWang XBellotti VDey ACarroll J(2019)Context-Aware Coproduction: Implications for Recommendation AlgorithmsInformation in Contemporary Society10.1007/978-3-030-15742-5_54(565-577)Online publication date: 13-Mar-2019
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Dillahunt TVeinot T(2018)Getting ThereACM Transactions on Computer-Human Interaction10.1145/323398525:5(1-39)Online publication date: 11-Oct-2018
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Doryab ABellotti VYousfi AWu SCarroll JDey A(2017)If It’s ConvenientProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31309131:3(1-28)Online publication date: 11-Sep-2017
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