Mobile App Recommendation using User`s Spatio-Temporal Context


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 9, pp. 615-620, Sep. 2013
10.3745/KTSDE.2013.2.9.615,   PDF Download:

Abstract

With the development of smartphones, the number of applications for smartphone increases sharply. As a result, users need to try several times to find their favorite apps. In order to solve this problem, we propose a recommendation system to provide an appropriate app list based on the user`s log information including time stamp, location, application list, and so on. The proposed approach learns three recommendation models including Naive-Bayesian model, SVM model, and Most-Frequent Usage model using temporal and spatial attributes. In order to figure out the best model, we compared the performance of these models with variant features, and suggest an hybrid method to improve the performance of single models.


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Cite this article
[IEEE Style]
Y. G. Kang, S. Y. Hwang, S. W. Park, S. W. Lee, "Mobile App Recommendation using User`s Spatio-Temporal Context," KIPS Transactions on Software and Data Engineering, vol. 2, no. 9, pp. 615-620, 2013. DOI: 10.3745/KTSDE.2013.2.9.615.

[ACM Style]
Young Gil Kang, Se Young Hwang, Sang Won Park, and Soo Won Lee. 2013. Mobile App Recommendation using User`s Spatio-Temporal Context. KIPS Transactions on Software and Data Engineering, 2, 9, (2013), 615-620. DOI: 10.3745/KTSDE.2013.2.9.615.