Context-Aware Recommender Systems in Vehicular Networks and Other Mobile Domains

Abstract

We give an overview of ideas for integrating context in recommender systems in general and specifically in various mobile application domains. Our main case study is an approach for vehicular ad-hoc networks (VANETs). The system recommends gas stations based on driver preferences, ratings of other users and context information such as the current location and fuel level of a car. We explain the main design issues behind our recommender. Our approach first filters items based on preferences and context, and then takes ratings of other users and additional information into account, which can be relayed from car to car in a VANET. We also outline other mobile scenarios for contextualized recommender systems: a system for recommending mobile applications based on user context, an approach to find relevant resources in mobile semantic personal information management, and a decentralized recommender system for personal digital assistants (PDAs) that has been successfully applied in a real world mobile city guide.