Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Capturing the effects of context on human performance in mobile computing systems
Personal and Ubiquitous Computing
AppAware: which mobile applications are hot?
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Identifying and utilizing contextual data in hybrid recommender systems
Proceedings of the fourth ACM conference on Recommender systems
App recommendation: a contest between satisfaction and temptation
Proceedings of the sixth ACM international conference on Web search and data mining
Proceedings of the 2013 international conference on Intelligent user interfaces
Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
Multi-objective mobile app recommendation: A system-level collaboration approach
Computers and Electrical Engineering
| Hi-index | 0.00 |
Most mobile platforms of today enable the users to install third-party applications through application portals or stores. As the number of applications available increases, the users of mobile devices find it challenging to find new and relevant applications. The fact that these applications usually are browsed and downloaded from a mobile device, which has a smaller screen compared to desktop computers, makes this information overload even more intense. Recommender systems aid users in finding relevant applications. A challenge with such systems is that they traditionally need a user profile in order to produce recommendations, known as the new user problem. In this paper we present a context-aware recommender system for mobile applications which produces recommendations from the first use. This paper introduces context-based recommender concepts and presents a prototype implementation of said concepts.