Location-based matchmaking: Difference between revisions

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== Introduction ==
== Introduction ==


'''Location-based matchmaking''' is related to [[location-aware computing]] and can mean two things:
'''Location-based matchmaking''' is related to [[location-aware computing]] and mostly describes applications that raise awareness of others' that share similar interests and who are in the same area. It can describe a computer algorithm that uses location data in order to match something, e.g., presence of person in a location and touristic sights nearby.
 
* It can describe a computer algorithm that uses location data in order to match something. E.g. presence of person in a location and touristic sights nearby
 
* It can describe applications that raise awareness of others' that share similar interests and who are in the same area.


== Bibliography and Links ==
== Bibliography and Links ==


* Michael Koch, Georg Groh, Christian Hillebrand: Mobile Communities – Extending Online Communities into the Real World, to appear in: Proc. Americas Conf. on Information Science (AMCIS2002), Dallas, TX, Aug. 2002. [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.9408&rep=rep1&type=pdf PDF]
* Michael Koch, Georg Groh, Christian Hillebrand: Mobile Communities – Extending Online Communities into the Real World, to appear in: Proc. Americas Conf. on Information Science (AMCIS2002), Dallas, TX, Aug. 2002. [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.9408&rep=rep1&type=pdf PDF]
 
* Pizzato, Luiz, 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 (RecSys ’10)'', 207–214. New York: ACM.
* Bao, Jie, Yu Zheng, and Mohamed F. Mokbel. 2015. “Location-Based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data.” ''Proceedings of the 20th International Conference on Advances in Geographic Information Systems''.
* Zheng, Yu. 2011. “Location-Based Social Networks: Users.” In ''Computing with Spatial Trajectories'', edited by Yu Zheng and Xiaofang Zhou, 243–276. New York: Springer.
* Sánchez, Pablo, and Alejandro Bellogín. 2021. “Point-of-Interest Recommender Systems Based on Location-Based Social Networks: A Survey from an Experimental Perspective.” ''ACM Computing Surveys''.


* [http://www.springerlink.com/index/684NP54833X6432R.pdf Lo-Fi matchmaking : a study of social pairing for backpackers]
* [http://www.springerlink.com/index/684NP54833X6432R.pdf Lo-Fi matchmaking : a study of social pairing for backpackers]

Latest revision as of 16:24, 6 July 2026

Draft

Introduction

Location-based matchmaking is related to location-aware computing and mostly describes applications that raise awareness of others' that share similar interests and who are in the same area. It can describe a computer algorithm that uses location data in order to match something, e.g., presence of person in a location and touristic sights nearby.

Bibliography and Links

  • Michael Koch, Georg Groh, Christian Hillebrand: Mobile Communities – Extending Online Communities into the Real World, to appear in: Proc. Americas Conf. on Information Science (AMCIS2002), Dallas, TX, Aug. 2002. PDF
  • Pizzato, Luiz, 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 (RecSys ’10), 207–214. New York: ACM.
  • Bao, Jie, Yu Zheng, and Mohamed F. Mokbel. 2015. “Location-Based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data.” Proceedings of the 20th International Conference on Advances in Geographic Information Systems.
  • Zheng, Yu. 2011. “Location-Based Social Networks: Users.” In Computing with Spatial Trajectories, edited by Yu Zheng and Xiaofang Zhou, 243–276. New York: Springer.
  • Sánchez, Pablo, and Alejandro Bellogín. 2021. “Point-of-Interest Recommender Systems Based on Location-Based Social Networks: A Survey from an Experimental Perspective.” ACM Computing Surveys.