skip to main content
research-article
Public Access

If It’s Convenient: Leveraging Context in Peer-to-Peer Variable Service Transaction Recommendations

Published:11 September 2017Publication History
Skip Abstract Section

Abstract

Peer-to-Peer Variable Service Transaction (P2P-VST) systems enable people to offer and receive help with a wide range of task types. However, such services are hampered by the difficulty of finding relevant and convenient opportunities for transactions in a timely fashion. Many transaction opportunities are missed as a consequence of members not being aware of offers and/or requests from people nearby or en route that match their needs and/or abilities. In this paper, we explore the impact of context-awareness on P2P-VSTs to address this problem. Using mobile technology and an in situ study, we evaluate how recommending service requests targeted at a person’s context impacts their willingness to enter a transaction. Our results show that, even when people have not actively volunteered for a service, they are significantly more likely to accept a transaction opportunity if it is convenient for them in terms of time and location. These findings demonstrate how context-aware technology holds the promise of increasing the efficiency and activity level in P2P-VST systems.

References

  1. 2016. SocialCar Open social transport network for urban approach to carpooling. http://socialcar-project.eu/download/D2_136.1_Social%20transport%20graph%20route%20planning%20and%20ride%20matching.pdf. (2016).Google ScholarGoogle Scholar
  2. Gregory Abowd, Anind Dey, Peter Brown, Nigel Davies, Mark Smith, and Pete Steggles. 1999. Towards a better understanding of context and context-awareness. In Handheld and ubiquitous computing. Springer, 304--307. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Florian Alt, Alireza Sahami Shirazi, Albrecht Schmidt, Urs Kramer, and Zahid Nawaz. 2010. Location-based crowdsourcing: extending crowdsourcing to the real world. In Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries. ACM, 13--22. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Tatiana PV Alves, Marcos RS Borges, and Adriana S Vivacqua. 2013. An environment to support the discovery of potential partners in a research group. In Computer Supported Cooperative Work in Design (CSCWD), 2013 IEEE 17th International Conference on. IEEE, 344--349.Google ScholarGoogle ScholarCross RefCross Ref
  5. Russell Belk. 2014. You are what you can access: Sharing and collaborative consumption online. Journal of Business Research 67, 8 (2014), 1595--1600.Google ScholarGoogle ScholarCross RefCross Ref
  6. Victoria Bellotti, Alexander Ambard, Daniel Turner, Christina Gossmann, Kamila Demkova, and John M Carroll. 2015. A muddle of models of motivation for using peer-to-peer economy systems. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 1085--1094. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Victoria Bellotti, Bo Begole, Ed H Chi, Nicolas Ducheneaut, Ji Fang, Ellen Isaacs, Tracy King, Mark W Newman, Kurt Partridge, Bob Price, et al. 2008. Activity-based serendipitous recommendations with the Magitti mobile leisure guide. In Proceedings of the sigchi conference on human factors in computing systems. ACM, 1157--1166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Victoria ME Bellotti, Sara Cambridge, Karen Hoy, Patrick C Shih, Lisa Renery Handalian, Kyungsik Han, and John M Carroll. 2014. Towards community-centered support for peer-to-peer service exchange: rethinking the timebanking metaphor. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2975--2984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Elisa Gonzalez Boix, Andoni Lombide Carreton, Christophe Scholliers, Tom Van Cutsem, Wolfgang De Meuter, and Theo D’Hondt. 2011. Flocks: enabling dynamic group interactions in mobile social networking applications. In Proceedings of the 2011 ACM Symposium on Applied Computing. ACM, 425--432. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Rachel Botsman and Roo Rogers. 2010. Beyond zipcar: Collaborative consumption. Harvard Business Review 88, 10 (2010), 30.Google ScholarGoogle Scholar
  11. Rachel Botsman and Roo Rogers. 2011. What’s mine is yours: how collaborative consumption is changing the way we live. (2011).Google ScholarGoogle Scholar
  12. Edgar S Cahn. 2000. No more throw-away people: The co-production imperative. Edgar Cahn.Google ScholarGoogle Scholar
  13. Edgar S Cahn and Jonathan Rowe. 1992. Time dollars: the new currency that enables Americans to turn their hidden resource-time-into personal security 8 community renewal. Rodale Press.Google ScholarGoogle Scholar
  14. John M Carroll. 2013. Co-production scenarios for mobile time banking. In International Symposium on End User Development. Springer, 137--152.Google ScholarGoogle ScholarCross RefCross Ref
  15. Blerim Cici, Athina Markopoulou, and Nikolaos Laoutaris. 2015. Designing an on-line ride-sharing system. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Ed Collom. 2008. Engagement of the elderly in time banking: The potential for social capital generation in an aging society. Journal of aging 8 social policy 20, 4 (2008), 414--436.Google ScholarGoogle ScholarCross RefCross Ref
  17. Ed Collom and Judith N Lasker. 2016. Equal time, equal value: Community currencies and time banking in the US. Routledge.Google ScholarGoogle Scholar
  18. Edward L Deci, Richard Koestner, and Richard M Ryan. 2001. Extrinsic rewards and intrinsic motivation in education: Reconsidered once again. Review of educational research 71, 1 (2001), 1--27.Google ScholarGoogle Scholar
  19. Fernando Diaz, Donald Metzler, and Sihem Amer-Yahia. 2010. Relevance and ranking in online dating systems. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. ACM, 66--73. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kristofer Dittmer. 2013. Local currencies for purposive degrowth? A quality check of some proposals for changing money-as-usual. Journal of Cleaner Production 54 (2013), 3--13.Google ScholarGoogle ScholarCross RefCross Ref
  21. Jane Dokko, Megan Mumford, and Diane Whitmore Schanzenbach. 2015. Workers and the Online Gig Economy. The Hamilton Project (2015).Google ScholarGoogle Scholar
  22. Paul Dourish. 2004. What we talk about when we talk about context. Personal and ubiquitous computing 8, 1 (2004), 19--30.Google ScholarGoogle Scholar
  23. Emilie A Dubois, Juliet B Schor, and Lindsey B Carfagna. 2014. New cultures of connection in a Boston time bank. Sustainable lifestyles and the quest for plentitude: Case studies of the new economy (2014), 95--124.Google ScholarGoogle Scholar
  24. Nathan Eagle and Alex Pentland. 2005. Social serendipity: Mobilizing social software. IEEE Pervasive Computing 4, 2 (2005), 28--34. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Maryam Fazel-Zarandi, Hugh J Devlin, Yun Huang, and Noshir Contractor. 2011. Expert recommendation based on social drivers, social network analysis, and semantic data representation. In Proceedings of the 2nd international workshop on information heterogeneity and fusion in recommender systems. ACM, 41--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Denzil Ferreira, Vassilis Kostakos, and Anind K Dey. 2015. AWARE: mobile context instrumentation framework. Frontiers in ICT 2 (2015), 6.Google ScholarGoogle ScholarCross RefCross Ref
  27. Eli J Finkel, Paul W Eastwick, Benjamin R Karney, Harry T Reis, and Susan Sprecher. 2012. Online dating: A critical analysis from the perspective of psychological science. Psychological Science in the Public Interest 13, 1 (2012), 3--66.Google ScholarGoogle ScholarCross RefCross Ref
  28. Eli J Finkel and Susan Sprecher. 2010. The Scientific Flaws of Online Dating Sites. Scientific American (2010).Google ScholarGoogle Scholar
  29. Lee Gregory. 2014. Resilience or resistance? Time banking in the age of austerity. Journal of Contemporary European Studies 22, 2 (2014), 171--183.Google ScholarGoogle ScholarCross RefCross Ref
  30. Wesam Mohamed Herbawi and Michael Weber. 2012. A genetic and insertion heuristic algorithm for solving the dynamic ridematching problem with time windows. In Proceedings of the 14th annual conference on Genetic and evolutionary computation. ACM, 385--392. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Lars Holmquist, Friedemann Mattern, Bernt Schiele, Petteri Alahuhta, Michael Beigl5, and Hans-W Gellersen. 2001. Smart-its friends: A technique for users to easily establish connections between smart artefacts. In Ubicomp 2001: Ubiquitous Computing. Springer, 116--122. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Torsten Hothorn, Kurt Hornik, and Achim Zeileis. 2006. Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical statistics 15, 3 (2006), 651--674.Google ScholarGoogle ScholarCross RefCross Ref
  33. Hyunggu Jung, Victoria Bellotti, Afsaneh Doryab, Dean Leitersdorf, Jiawei Chen, Benjamin V Hanrahan, Sooyeon Lee, Dan Turner, Anind K Dey, and John M Carroll. 2016. ’MASTerful’Matchmaking in Service Transactions: Inferred Abilities, Needs and Interests versus Activity Histories. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 1644--1655. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Nikita Jaiman, Randy Tandriansyah, Cen Chen, Hoong Chuin Lau, Deepthi Chander, and Koustuv Dasgupta. 2016. Campus-scale mobile crowd-tasking: Deployment 8 behavioral insights. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work 8 Social Computing. ACM, 800--812. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Yongsung Kim, Emily Harburg, Shana Azria, Aaron Shaw, Elizabeth Gerber, Darren Gergle, and Haoqi Zhang. 2016. Studying the Effects of Task Notification Policies on Participation and Outcomes in On-the-go Crowdsourcing. (2016).Google ScholarGoogle Scholar
  36. Martin Knapp, Annette Bauer, Margaret Perkins, and Tom Snell. 2010. Building community capacity: making an economic case. (2010).Google ScholarGoogle Scholar
  37. Mikko Laamanen, Stefan Wahlen, and Mario Campana. 2015. Mobilising collaborative consumption lifestyles: A comparative frame analysis of time banking. International journal of consumer studies 39, 5 (2015), 459--467.Google ScholarGoogle Scholar
  38. DongWoo Lee and Steve HL Liang. 2011. Crowd-sourced carpool recommendation based on simple and efficient trajectory grouping. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on Computational Transportation Science. ACM, 12--17. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Lei Li and Tao Li. 2012. MEET: a generalized framework for reciprocal recommender systems. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 35--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Allan Luks and Peggy Payne. 2001. The healing power of doing good. iUniverse.Google ScholarGoogle Scholar
  41. Jochen Malinowski, Tobias Keim, Oliver Wendt, and Tim Weitzel. 2006. Matching people and jobs: A bilateral recommendation approach. In System Sciences, 2006. HICSS’06. Proceedings of the 39th Annual Hawaii International Conference on, Vol. 6. IEEE, 137c--137c. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Julia M Mayer, Starr Roxanne Hiltz, Louise Barkhuus, Kaisa Väänänen, and Quentin Jones. 2016. Supporting opportunities for context-aware social matching: An experience sampling study. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2430--2441. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Joseph McCarthy and Eric Meidel. 1999. ACTIVE MAP: A visualization tool for location awareness to support informal interactions. In Handheld and Ubiquitous Computing. Springer, 158--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Debnath Mukherjee, Snehasis Banerjee, and Prateep Misra. 2012. Ad-hoc ride sharing application using continuous sparql queries. In Proceedings of the 21st International Conference on World Wide Web. ACM, 579--580. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Lucie K Ozanne. 2010. Learning to exchange time: Benefits and obstacles to time banking. (2010).Google ScholarGoogle Scholar
  46. Per Persson and Younghee Jung. 2005. Nokia sensor: from research to product. In Proceedings of the 2005 conference on Designing for User eXperience. AIGA: American Institute of Graphic Arts, 53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Luiz Pizzato, Tomek Rej, Thomas Chung, Irena Koprinska, and Judy Kay. 2010. RECON: a reciprocal recommender for online dating. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 207--214. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Debbie Richards, Meredith Taylor, and Peter Busch. 2008. Expertise recommendation: A two-way knowledge communication channel. In Autonomic and Autonomous Systems, 2008. ICAS 2008. Fourth International Conference on. IEEE, 35--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Tomoyo Sasao, Shin’ichi Konomi, Vassilis Kostakos, Keisuke Kuribayashi, and Jorge Goncalves. 2017. Community Reminder: Participatory contextual reminder environments for local communities. International Journal of Human-Computer Studies 102 (2017), 41--53. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Gill Seyfang. 2002. Tackling social exclusion with community currencies: learning from LETS to Time Banks. International Journal of Community Currency Research 6 (2002).Google ScholarGoogle Scholar
  51. Gill Seyfang. 2003. âĂŸWith a little help from my friends.âĂŹEvaluating time banks as a tool for community self-help. Local Economy 18, 3 (2003), 257--264.Google ScholarGoogle ScholarCross RefCross Ref
  52. Gill Seyfang and Noel Longhurst. 2013. Growing green money? Mapping community currencies for sustainable development. Ecological Economics 86 (2013), 65--77.Google ScholarGoogle ScholarCross RefCross Ref
  53. Patrick C Shih, Victoria Bellotti, Kyungsik Han, and John M Carroll. 2015. Unequal time for unequal value: Implications of differing motivations for participation in timebanking. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 1075--1084. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Bryant A Stamford. 1976. Validity and reliability of subjective ratings of perceived exertion during work. Ergonomics 19,1 (1976), 53--60.Google ScholarGoogle ScholarCross RefCross Ref
  55. Michael Terry, Elizabeth D Mynatt, Kathy Ryall, and Darren Leigh. 2002. Social net: using patterns of physical proximity over time to infer shared interests. In CHI’02 extended abstracts on Human factors in computing systems. ACM, 816--817. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Lukáš Válek and Veronika Jašíková. 2013. Time bank and sustainability: The permaculture approach. Procedia-Social and Behavioral Sciences 92 (2013), 986--991.Google ScholarGoogle ScholarCross RefCross Ref
  57. Mark Wardman. 1988. A comparison of revealed preference and stated preference models of travel behaviour. Journal of transport economics and policy (1988), 71--91.Google ScholarGoogle Scholar
  58. Svetlana Yarosh, Tara Matthews, and Michelle Zhou. 2012. Asking the right person: supporting expertise selection in the enterprise. In Proceedings of the sigchi conference on human factors in computing systems. ACM, 2247--2256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Desheng Zhang, Tian He, Fan Zhang, Mingming Lu, Yunhuai Liu, Haengju Lee, and Sang H Son. 2016. Carpooling Service for Large-Scale Taxicab Networks. ACM Transactions on Sensor Networks (TOSN) 12, 3 (2016), 18. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. If It’s Convenient: Leveraging Context in Peer-to-Peer Variable Service Transaction Recommendations

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
          Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 1, Issue 3
          September 2017
          2023 pages
          EISSN:2474-9567
          DOI:10.1145/3139486
          Issue’s Table of Contents

          Copyright © 2017 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 11 September 2017
          • Accepted: 1 June 2017
          • Revised: 1 May 2017
          • Received: 1 February 2017
          Published in imwut Volume 1, Issue 3

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • Refereed

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader