The Anatomy of Mobile Location-Based Recommender Systems


The widespread adoption of smartphones is now putting both the Internet and sensor-rich hardware into the pockets of millions. While recommender systems have become the norm on many web sites, many mobile systems have historically been built as location-based services. However, these devices are becoming the ideal interface for recommender systems that help users discover, explore, and learn about their physical surroundings. In this chapter, we review the main components of a mobile location-based recommender system: the data that can be used to learn about users and items, the algorithms that have been applied to recommending venues, and the techniques that researchers have used to evaluate the quality of these recommendations, using research that is sourced from a variety of fields. This chapter closes by highlighting a number of opportunities and open challenges related to building future mobile recommender systems.


Global Position System Recommender System Mobile System Supervise Learning Approach Implicit Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK

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