Abstract
Coproduction is an important form of service exchange in local community where members perform and receive services among each other on non-profit basis. Local coproduction systems enhance community connections and re-energize neighborhoods but face difficulties matching relevant and convenient transaction opportunities. Context-aware recommendations can provide promising solutions, but are so far limited to matching spatio-temporal and static user contexts. By analyzing data from a transportation-share app during a 3-week study with 23 participants, we extend the design scope for context-aware recommendation algorithms to include important community-based parameters such as sense of community. We find that inter- and intra-relationships between spatio-temporal and community-based social contexts significantly impact users’ motivation to request or provide service. The results provide novel insights for designing context-aware recommendation algorithms for community coproduction services.
| Original language | English (US) |
|---|---|
| Title of host publication | Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings |
| Editors | Michelle H. Martin, Natalie Greene Taylor, Bonnie Nardi, Caitlin Christian-Lamb |
| Publisher | Springer Verlag |
| Pages | 565-577 |
| Number of pages | 13 |
| ISBN (Print) | 9783030157418 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Event | 14th International Conference on Information in Contemporary Society, iConference 2019 - Washington, United States Duration: Mar 31 2019 → Apr 3 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11420 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 14th International Conference on Information in Contemporary Society, iConference 2019 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 3/31/19 → 4/3/19 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Context-Aware Coproduction : Implications for Recommendation Algorithms. / Chen, Jiawei; Doryab, Afsaneh; Hanrahan, Benjamin Vincent; Yousfi, Alaaeddine; Beck, Jordan; Wang, Xiying; Bellotti, Victoria; Dey, Anind K.; Carroll, John.
Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings. ed. / Michelle H. Martin; Natalie Greene Taylor; Bonnie Nardi; Caitlin Christian-Lamb. Springer Verlag, 2019. p. 565-577 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11420 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Context-Aware Coproduction
T2 - Implications for Recommendation Algorithms
AU - Chen, Jiawei
AU - Doryab, Afsaneh
AU - Hanrahan, Benjamin Vincent
AU - Yousfi, Alaaeddine
AU - Beck, Jordan
AU - Wang, Xiying
AU - Bellotti, Victoria
AU - Dey, Anind K.
AU - Carroll, John
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Coproduction is an important form of service exchange in local community where members perform and receive services among each other on non-profit basis. Local coproduction systems enhance community connections and re-energize neighborhoods but face difficulties matching relevant and convenient transaction opportunities. Context-aware recommendations can provide promising solutions, but are so far limited to matching spatio-temporal and static user contexts. By analyzing data from a transportation-share app during a 3-week study with 23 participants, we extend the design scope for context-aware recommendation algorithms to include important community-based parameters such as sense of community. We find that inter- and intra-relationships between spatio-temporal and community-based social contexts significantly impact users’ motivation to request or provide service. The results provide novel insights for designing context-aware recommendation algorithms for community coproduction services.
AB - Coproduction is an important form of service exchange in local community where members perform and receive services among each other on non-profit basis. Local coproduction systems enhance community connections and re-energize neighborhoods but face difficulties matching relevant and convenient transaction opportunities. Context-aware recommendations can provide promising solutions, but are so far limited to matching spatio-temporal and static user contexts. By analyzing data from a transportation-share app during a 3-week study with 23 participants, we extend the design scope for context-aware recommendation algorithms to include important community-based parameters such as sense of community. We find that inter- and intra-relationships between spatio-temporal and community-based social contexts significantly impact users’ motivation to request or provide service. The results provide novel insights for designing context-aware recommendation algorithms for community coproduction services.
UR - http://www.scopus.com/inward/record.url?scp=85064056616&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064056616&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15742-5_54
DO - 10.1007/978-3-030-15742-5_54
M3 - Conference contribution
AN - SCOPUS:85064056616
SN - 9783030157418
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 565
EP - 577
BT - Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings
A2 - Martin, Michelle H.
A2 - Taylor, Natalie Greene
A2 - Nardi, Bonnie
A2 - Christian-Lamb, Caitlin
PB - Springer Verlag
ER -