Editing Recommender system (section)

=== Mobile recommender systems ===
{{further|Location based recommendation}}
Mobile recommender systems make use of internet-accessing [[Smartphone|smartphones]] to offer personalized, context-sensitive recommendations. This is a particularly difficult area of research as mobile data is more complex than data that recommender systems often have to deal with. It is heterogeneous, noisy, requires spatial and temporal auto-correlation, and has validation and generality problems.<ref name="taxirecommender">{{cite conference |author1=Yong Ge |author2=Hui Xiong |author3=Alexander Tuzhilin |author4=Keli Xiao |author5=Marco Gruteser |author6=Michael J. Pazzani |year = 2010 |title = An Energy-Efficient Mobile Recommender System |conference = Proceedings of the 16th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining |url = http://www.winlab.rutgers.edu/~gruteser/papers/KDD10.pdf |publisher = [[Association for Computing Machinery|ACM]] |location = [[New York City|New York City, New York]] |pages = 899–908 |access-date = 2011-11-17 }}
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There are three factors that could affect the mobile recommender systems and the accuracy of prediction results: the context, the recommendation method and privacy.<ref>{{cite journal |last1=Pimenidis |first1=Elias |last2=Polatidis |first2=Nikolaos |last3=Mouratidis |first3=Haralambos |title=Mobile recommender systems: Identifying the major concepts |journal=Journal of Information Science |volume=45 |issue=3 |date=3 August 2018 |pages=387–397 |doi=10.1177/0165551518792213|arxiv=1805.02276 |s2cid=19209845 }}</ref> Additionally, mobile recommender systems suffer from a transplantation problem – recommendations may not apply in all regions (for instance, it would be unwise to recommend a recipe in an area where all of the ingredients may not be available).

One example of a mobile recommender system are the approaches taken by companies such as [[Uber]] and [[Lyft]] to generate driving routes for taxi drivers in a city.<ref name="taxirecommender"/> This system uses GPS data of the routes that taxi drivers take while working, which includes location (latitude and longitude), time stamps, and operational status (with or without passengers). It uses this data to recommend a list of pickup points along a route, with the goal of optimizing occupancy times and profits.