Recommender System - Mobile Recommender Systems

Mobile Recommender Systems

One growing area of research in the area of recommender systems is mobile recommender systems. With the increasing ubiquity of internet-accessing smart phones, it is now possible to offer personalized, context-sensitive recommendations. This is a particularly difficult area of research as mobile data is more complex than recommender systems often have to deal with (it is heterogeneous, noisy, requires spatial and temporal auto-correlation, and has validation and generality problems ). 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 is one that offers potentially profitable driving routes for taxi drivers in a city. This system takes as input data in the form of GPS traces of the routes that taxi drivers took while working, which include location (latitude and longitude), time stamps, and operational status (with or without passengers). It then recommends a list of pickup points along a route that will lead to optimal occupancy times and profits. This type of system is obviously location-dependent, and as it must operate on a handheld or embedded device, the computation and energy requirements must remain low.

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