Abstract
Due to the large number of applications in the mobile phones, users usually go through a fixed menu hierarchy to find a specific interesting application. Hence, in our previous research, we realized the proactive mobile phone application recommendation using co-clustering and demonstrated the promising recommendation performance on a smartphone. The approach first autonomously extracts user’s behavioral patterns from the usage log of user interactions with the device as well as environments and then recommends potential applications that might be interesting to the user at the corresponding specific situation. In this paper, as a follow-up to this novel platform of intelligent smartphone-based situation-awareness, we investigate sophisticated methodologies that lead to better performance. To achieve this goal, we considered various co-clustering algorithms with different data transformations and weighting schemes for simulated mobile phone usage data. Through non-exhaustive, but pretty comprehensive experimental setting, we find what specific co-clustering algorithms with what specific data transformations and weighting schemes improve accuracy performance in extracting specific user patterns.
Original language | American English |
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Title of host publication | Proceedings of the International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems |
DOIs | |
State | Published - Jun 7 2012 |
Keywords
- Co-clustering
- Data transformation
- Pattern extraction
- Recommendation system
- Situation aware
- Weighting
DC Disciplines
- Databases and Information Systems