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.
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Cho, H., Mandava, D., Liu, Q., Chen, L., Jeong, S., Cheng, D. (2012). Situation-Aware on Mobile Phone Using Co-clustering: Algorithms and Extensions. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_29
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DOI: https://doi.org/10.1007/978-3-642-31087-4_29
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