ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
IEEE Transactions on Knowledge and Data Engineering
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Data mining emotion in social network communication: Gender differences in MySpace
Journal of the American Society for Information Science and Technology
Fusing mobile, sensor, and social data to fully enable context-aware computing
Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications
Adapting Batch Learning Algorithms Execution in Ubiquitous Devices
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
WhozThat? evolving an ecosystem for context-aware mobile social networks
IEEE Network: The Magazine of Global Internetworking
Collective intelligence as mechanism of medical diagnosis: The iPixel approach
Expert Systems with Applications: An International Journal
Annotating mobile phone location data with activity purposes using machine learning algorithms
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
A 2010 survey (Nielsen) showed that 22.7% of the time spent on the Internet is on a social network. Moreover, there is an increasing demand to access social networks by mobile phones, i.e., around 30% globally. Social networking has become a reality, and it generates an incredible amount of information that is sometimes difficult for users to process, especially from mobile phones. Several links, activities, and recommendations are proposed by networked friends every hour, which together are nearly impossible to manage. There is a need to filter and make accessible such information to users, which is the motivation behind developing a mobile recommender that exploits social network information. Thus, in this paper, we propose the design and the implementation of a SOcial Mobile Activity Recommender (SOMAR) that can integrate Facebook social network mobile data and sensor data to propose activities to the user (e.g., concert or computer science seminar). The recommendations are completely calculated in situ in the mobile device with an embedded data mining component. We analyze how to compute and update the social graph in case of changes in social relationships or user context. The paper also presents a case study to analyze the performance of the method.