2019/06/07 by Felix Beierle, Beierle, Felix, Tobias Eichinger +1
Computer Science · #Caching and Content Delivery #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1906.03114
openalex publication_date 2019/06/07 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Typically, recommender systems from any domain, be it movies, music,\nrestaurants, etc., are organized in a centralized fashion. The service provider\nholds all the data, biases in the recommender algorithms are not transparent to\nthe user, and the service providers often create lock-in effects making it\ninconvenient for the user to switch providers. In this paper, we argue that the\nuser's smartphone already holds a lot of the data that feeds into typical\nrecommender systems for movies, music, or POIs. With the ubiquity of the\nsmartphone and other users in proximity in public places or public\ntransportation, data can be exchanged directly between users in a\ndevice-to-device manner. This way, each smartphone can build its own database\nand calculate its own recommendations. One of the benefits of such a system is\nthat it is not restricted to recommendations for just one user - ad-hoc group\nrecommendations are also possible. While the infrastructure for such a platform\nalready exists - the smartphones already in the palms of the users - there are\nchallenges both with respect to the mobile recommender system platform as well\nas to its recommender algorithms. In this paper, we present a mobile\narchitecture for the described system - consisting of data collection, data\nexchange, and recommender system - and highlight its challenges and\nopportunities.\n