Context-Aware Coproduction: Implications for Recommendation Algorithms

• Published in: Information in Contemporary Society pp. 565 - 577
• Main Authors: Chen, Jiawei, Doryab, Afsaneh, Hanrahan, Benjamin V., Yousfi, Alaaeddine, Beck, Jordan, Wang, Xiying, Bellotti, Victoria, Dey, Anind K., Carroll, John M.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Community; Context-awareness; Coproduction
• ISBN: 3030157415; 9783030157418
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-15742-5_54

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.