Context-Aware Coproduction: Implications for Recommendation Algorithms

Jiawei Chen; Afsaneh Doryab; Benjamin Vincent Hanrahan; Alaaeddine Yousfi; Jordan Beck; Xiying Wang; Victoria Bellotti; Anind K. Dey; John Carroll

doi:10.1007/978-3-030-15742-5_54

Context-Aware Coproduction: Implications for Recommendation Algorithms

Jiawei Chen, Afsaneh Doryab, Benjamin Vincent Hanrahan, Alaaeddine Yousfi, Jordan Beck, Xiying Wang, Victoria Bellotti, Anind K. Dey, John Carroll

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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	https://doi.org/10.1007/978-3-030-15742-5_54
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

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-15742-5_54

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Chen, J., Doryab, A., Hanrahan, B. V., Yousfi, A., Beck, J., Wang, X., Bellotti, V., Dey, A. K., & Carroll, J. (2019). Context-Aware Coproduction: Implications for Recommendation Algorithms. In M. H. Martin, N. G. Taylor, B. Nardi, & C. Christian-Lamb (Eds.), Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings (pp. 565-577). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11420 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-15742-5_54

Chen, Jiawei ; Doryab, Afsaneh ; Hanrahan, Benjamin Vincent ; Yousfi, Alaaeddine ; Beck, Jordan ; Wang, Xiying ; Bellotti, Victoria ; Dey, Anind K. ; Carroll, John. / Context-Aware Coproduction : Implications for Recommendation Algorithms. Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings. editor / Michelle H. Martin ; Natalie Greene Taylor ; Bonnie Nardi ; Caitlin Christian-Lamb. Springer Verlag, 2019. pp. 565-577 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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{\textquoteright} motivation to request or provide service. The results provide novel insights for designing context-aware recommendation algorithms for community coproduction services.",

author = "Jiawei Chen and Afsaneh Doryab and Hanrahan, {Benjamin Vincent} and Alaaeddine Yousfi and Jordan Beck and Xiying Wang and Victoria Bellotti and Dey, {Anind K.} and John Carroll",

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Chen, J, Doryab, A, Hanrahan, BV, Yousfi, A, Beck, J, Wang, X, Bellotti, V, Dey, AK & Carroll, J 2019, Context-Aware Coproduction: Implications for Recommendation Algorithms. in MH Martin, NG Taylor, B Nardi & C Christian-Lamb (eds), Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11420 LNCS, Springer Verlag, pp. 565-577, 14th International Conference on Information in Contemporary Society, iConference 2019, Washington, United States, 3/31/19. https://doi.org/10.1007/978-3-030-15742-5_54

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

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AU - Chen, Jiawei

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AU - Hanrahan, Benjamin Vincent

AU - Yousfi, Alaaeddine

AU - Beck, Jordan

AU - Wang, Xiying

AU - Bellotti, Victoria

AU - Dey, Anind K.

AU - Carroll, John

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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.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings

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Chen J, Doryab A, Hanrahan BV, Yousfi A, Beck J, Wang X et al. Context-Aware Coproduction: Implications for Recommendation Algorithms. In Martin MH, Taylor NG, Nardi B, Christian-Lamb C, editors, Information in Contemporary Society - 14th International Conference, iConference 2019, Proceedings. Springer Verlag. 2019. p. 565-577. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-15742-5_54