Context-Aware Coproduction: Implications for Recommendation Algorithms

  • Jiawei ChenEmail author
  • Afsaneh Doryab
  • Benjamin V. Hanrahan
  • Alaaeddine Yousfi
  • Jordan Beck
  • Xiying Wang
  • Victoria Bellotti
  • Anind K. Dey
  • John M. Carroll
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11420)


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.


Context-awareness Community Coproduction 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jiawei Chen
    • 1
    Email author
  • Afsaneh Doryab
    • 2
  • Benjamin V. Hanrahan
    • 1
  • Alaaeddine Yousfi
    • 3
  • Jordan Beck
    • 1
  • Xiying Wang
    • 4
  • Victoria Bellotti
    • 5
  • Anind K. Dey
    • 6
  • John M. Carroll
    • 1
  1. 1.The Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Carnegie Mellon University HCI InstitutePittsburghUSA
  3. 3.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany
  4. 4.WorkSpanFoster CityUSA
  5. 5.LyftSan FranciscoUSA
  6. 6.University of WashingtonSeattleUSA

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