Towards matching improvement between spatio-temporal tasks and workers in mobile crowdsourcing market systems

Crowdsourcing market systems (CMS) are platforms that enable one to publish tasks that others are intended to accomplished. Usually, these are systems where users, called workers, perform tasks using desktop computers. Recently, mobile CMSs have appeared with tasks that exploit the mobility and the location of workers. For example, if a third party system requires a picture of a given place, it may publish a task asking for some worker to go there, take this picture and upload it. One problem of CMSs is that the more tasks they have, the harder it is for workers to find and choose one they are interested in. Besides, workers who accomplish tasks may have no particular experience and consequently provide bad results for tasks. In order to improve the matching between workers and spatio-temporal tasks in mobile CMSs, we propose a conceptual framework that consists of two mechanisms. One considers the requirements of a task for selecting suitable workers, while the other recommends tasks for a worker according to his preferences and skills. As a result, workers spend less time searching tasks, more working on it, providing results with higher quality.

[1]  Jaime G. Carbonell,et al.  Towards Task Recommendation in Micro-Task Markets , 2011, Human Computation.

[2]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[3]  Aniket Kittur,et al.  CrowdForge: crowdsourcing complex work , 2011, UIST.

[4]  Eric Horvitz,et al.  Signals in the Silence: Models of Implicit Feedback in a Recommendation System for Crowdsourcing , 2014, AAAI.

[5]  Alireza Sahami Shirazi,et al.  Location-based crowdsourcing: extending crowdsourcing to the real world , 2010, NordiCHI.

[6]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[7]  Eric Horvitz,et al.  Task routing for prediction tasks , 2012, AAMAS.

[8]  Michael J. Pazzani,et al.  Content-Based Recommendation Systems , 2007, The Adaptive Web.

[9]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[11]  Deepak Ganesan,et al.  mCrowd: a platform for mobile crowdsourcing , 2009, SenSys '09.

[12]  Injong Rhee,et al.  SLAW: A New Mobility Model for Human Walks , 2009, IEEE INFOCOM 2009.

[13]  Kyumin Lee,et al.  Exploring Millions of Footprints in Location Sharing Services , 2011, ICWSM.

[14]  Ahmed Eldawy,et al.  LARS: A Location-Aware Recommender System , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[15]  Eric Horvitz,et al.  Volunteering Versus Work for Pay: Incentives and Tradeoffs in Crowdsourcing , 2013, HCOMP.

[16]  Pasquale Lops,et al.  Content-based Recommender Systems: State of the Art and Trends , 2011, Recommender Systems Handbook.

[17]  Deepak Ganesan,et al.  The Role of Super Agents in Mobile Crowdsourcing , 2012, HCOMP@AAAI.

[18]  Eric Sun,et al.  Location3: How Users Share and Respond to Location-Based Data on Social , 2011, ICWSM.

[19]  Huan Liu,et al.  Exploring temporal effects for location recommendation on location-based social networks , 2013, RecSys.

[20]  Ramesh Govindan,et al.  Medusa: a programming framework for crowd-sensing applications , 2012, MobiSys '12.

[21]  Dana Chandler,et al.  Labor Allocation in Paid Crowdsourcing: Experimental Evidence on Positioning, Nudges and Prices , 2011, Human Computation.

[22]  Eric Sun,et al.  Location 3 : How Users Share and Respond to Location-Based Data on Social Networking Sites , 2011 .

[23]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[24]  Lei Chen,et al.  GeoTruCrowd: trustworthy query answering with spatial crowdsourcing , 2013, SIGSPATIAL/GIS.

[25]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[26]  George Karypis,et al.  A Comprehensive Survey of Neighborhood-based Recommendation Methods , 2011, Recommender Systems Handbook.

[27]  Kwong-Sak Leung,et al.  Task recommendation in crowdsourcing systems , 2012, CrowdKDD '12.

[28]  Mor Naaman,et al.  The motivations and experiences of the on-demand mobile workforce , 2014, CSCW.

[29]  Ugur Demiryurek,et al.  Maximizing the number of worker's self-selected tasks in spatial crowdsourcing , 2013, SIGSPATIAL/GIS.

[30]  Gianluca Demartini,et al.  Pick-a-crowd: tell me what you like, and i'll tell you what to do , 2013, CIDR.

[31]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .