CrowdRec Contribution to SIREMTI Workshop / MobiCASE

Author: Benjamin Kille

The 7th EAI International Conference on Mobile Computing, Applications and Services took place on 12-13 November in Berlin. The Workshop on Situation Recognition by Mining Temporal Information was held as part of the event.

We contributed a paper in collaboration with Fabian Abel (XING) and Balázs Hidasi (Gravity R&D). “Using Interaction Signals for Job Recommendation” describes and evaluates recorded interactions between professionals and job offers. In addition, a user inquiry delivers the ground truth on how relevant professionals perceived suggested job offers. We investigated three types of events: clicking, bookmarking, and replying. Professionals can reply to suggested jobs in three ways:

  • messaging the recruiter
  • request additional information
  • follow a link to an application form

Our data show that replying correlates with high relevant suggestions. Bookmarks correlate predominantly with medium relevant suggestions. Individual clicks have a wide-spread distribution. They fail to differentiate highly relevant from irrelevant jobs. As the professional repeatedly click on suggested jobs, the correlation tends to more relevant jobs. slides

The conference provided a diverse set of talks. Topics included the Internet of Things, Wireless Mesh Networks, 3D-Printing, and QR Codes. Besides our contribution, there were two papers discussing recommender systems. Nadeem Jamali (University of Saskatchewan) introduced CSSWare, a framework to use crowd-sourced data to enhance recommendation. Mouzhi Ge (Free University of Bozen-Bolzano) described how logging devices enable intelligent dining recommendations. Their work highlighted the connection between life logging and decision support in the domain of nutrition and eating.

The diverse mixture of research provided many chances for interesting conversations during lunch an the breaks.

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