Abstract
Recommender systems recommend new movies, music, restaurants, etc. Typically, service providers organize such systems in a centralized way, holding all the data. Biases in the recommender systems are not transparent to the user and lock-in effects might make it inconvenient for the user to switch providers. In this paper, we present the concept, design, and implementation of MobRec, a mobile platform that decentralizes the data collection, data storage, and recommendation process. MobRec's architecture does not need any backend and solely consists of the users' smartphones, which already contain the users' preferences and ratings. Being in proximity in public places or public transportation, data is exchanged in a device-to-device manner, building local databases that can recommend new items. One of biggest challenges of such a system is the implementation of unobtrusive device-to-device data exchange on off-the-shelf Android devices and iPhones. MobRec facilitates such data exchange, building on Google Nearby Messages with Bluetooth Low Energy. We achieve the successful exchange of data within 3 to 4 minutes, making it suitable for the described scenario. We demonstrate the feasibility of decentralized recommender systems and provide blueprints for the development of seamless multi-platform device-to-device communication.
Highlights
IntroductionThey recommend items from different domains, for example, media to consume (e.g., Spotify, Netflix) or points of interests (POIs) to visit (e.g., Yelp, Google Maps)
Existing providers typically operate in a centralized manner: the service provider holds all the data and recommends items based on algorithms that are not visible to the user
The ubiquity of Internet connections allows for using a service that requires Internet connection
Summary
They recommend items from different domains, for example, media to consume (e.g., Spotify, Netflix) or points of interests (POIs) to visit (e.g., Yelp, Google Maps). Existing providers typically operate in a centralized manner: the service provider holds all the data and recommends items based on algorithms that are not visible to the user. This can introduce certain limitations and biases. Possible biases could be that the recommender algorithms favor items that create more profit for the service provider. The mentioned service providers are interested in retaining their user base and create lock-in effects.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
