Context-Aware Coproduction: Implications for Recommendation Algorithms

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

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

Context-Aware Coproduction: Implications for Recommendation Algorithms

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

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

1 Scopus citations

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 - 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/Territory	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

Cite this

Chen, J., Doryab, A., Hanrahan, B. V., Yousfi, A., Beck, J., Wang, X., Bellotti, V., Dey, A. K., & Carroll, J. M. (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 V. et al. / 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)).

@inproceedings{17508a8dd7ca4b17b8f3c04bec91013d,

title = "Context-Aware Coproduction: Implications for Recommendation Algorithms",

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 V.} and Alaaeddine Yousfi and Jordan Beck and Xiying Wang and Victoria Bellotti and Dey, {Anind K.} and Carroll, {John M.}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 14th International Conference on Information in Contemporary Society, iConference 2019 ; Conference date: 31-03-2019 Through 03-04-2019",

year = "2019",

doi = "10.1007/978-3-030-15742-5_54",

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isbn = "9783030157418",

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pages = "565--577",

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address = "Germany",

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Chen, J, Doryab, A, Hanrahan, BV, Yousfi, A, Beck, J, Wang, X, Bellotti, V, Dey, AK & Carroll, JM 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 V. et al.
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 - 14th International Conference on Information in Contemporary Society, iConference 2019

AU - Chen, Jiawei

AU - Doryab, Afsaneh

AU - Hanrahan, Benjamin V.

AU - Yousfi, Alaaeddine

AU - Beck, Jordan

AU - Wang, Xiying

AU - Bellotti, Victoria

AU - Dey, Anind K.

AU - Carroll, John M.

PY - 2019

Y1 - 2019

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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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

Y2 - 31 March 2019 through 3 April 2019

ER -

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

Context-Aware Coproduction: Implications for Recommendation Algorithms

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