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

Details

Event ACM Conference on Human Factors in Computing Systems CHI2016

Authors

Victoria Bellotti
Technical Publications
May 7th 2016
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 peoples historical offers or requests to the category of a current transaction request.

Citation

Jung, H.; Bellotti, V.; Doryab, A.; Leitersdorf, D.; Chen, J.; Hanrahan, B.; Sooyeon, L.; Turner, D.; Dey, A. K.; Carroll, J. M. MASTerful Matchmaking in Service Transactions: Inferred Abilities, Needs and Interests versus Activity Histories. ACM Conference on Human Factors in Computing Systems CHI2016.; San Jose, CA USA. Date of Talk: 2016-05-07

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