Abstract
Due to a huge amount of mobile applications (abbreviated as Apps), for Apps providers, the usage preferences of Apps are important in recommending Apps, downloading Apps and promoting Apps. We predict and quantize users’ dynamic preferences by exploring their usage traces of Apps. To address the dynamic preference prediction problem, we propose Mode-based Prediction (abbreviated as MBP) and Reference-based Prediction (abbreviated as RBP) algorithms. Both MBP and RBP consist of two phases: the trend detection phase and the change estimation phase. In the trend detection phase, both algorithms determine whether the preference of an App is increasing or decreasing. Then, in the change estimation phase, the amount of preference change is calculated. In particular, MBP adopts users’ current usage mode (active or inactive), and then estimates the amount of change via our proposed utility model. On the other hand, RBP calculates an expected number of usage as a reference, and then builds a probabilistic model to estimate the change of preference by comparing the real usage and the reference. We conduct comprehensive experiments using two App usage traces and one music listening log, the Last.fm dataset, to validate our proposed algorithms. The experimental results show that both MBP and RBP outperform the usage-based method that is based solely on the number of usages.
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References
Dong, Y., Ke, Q., Rao, J., Wang, B., Wu, B.: Random walk based resource allocation: Predicting and recommending links in cross-operator mobile communication networks. In: 2011 IEEE 11th International Conference on Data Mining Workshops, ICDMW, Vancouver, BC, Canada, December 11, pp. 358–365 (2011)
Lymberopoulos, D., Zhao, P., König, A.C., Berberich, K., Liu, J.: Location-aware click prediction in mobile local search. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, October 24-28, pp. 413–422 (2011)
Guy, I., Zwerdling, N., Ronen, I., Carmel, D., Uziel, E.: Social media recommendation based on people and tags. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, Geneva, Switzerland, July 19-23, pp. 194–201 (2010)
Yan, B., Chen, G.: Appjoy: personalized mobile application discovery. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, MobiSys 2011, Bethesda, MD, USA, June 28-July 1, pp. 113–126 (2011)
Shi, K., Ali, K.: Getjar mobile application recommendations with very sparse datasets. In: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, Beijing, China, August 12-16, pp. 204–212 (2012)
Dror, G., Koenigstein, N., Koren, Y., Weimer, M.: The yahoo! music dataset and kdd-cup 2011. In: KDD-Cup Workshop (2011)
Koren, Y.: Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28-July 1, pp. 447–456 (2009)
Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N.S., Hurst, M.: Patterns of cascading behavior in large blog graphs. In: Proceedings of the Seventh SIAM International Conference on Data Mining, Minneapolis, Minnesota, USA, April 26-28 (2007)
Fei, H., Jiang, R., Yang, Y., Luo, B., Huan, J.: Content based social behavior prediction: a multi-task learning approach. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, October 24-28, pp. 995–1000 (2011)
Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, Geneva, Switzerland, July 19-23, pp. 210–217 (2010)
Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long- and short-term preference fusion. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, July 25-28, pp. 723–732 (2010)
Celma, O.: Music Recommendation and Discovery in the Long Tail. Springer (2010)
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Liao, ZX., Peng, WC., Yu, P.S. (2013). Mining Usage Traces of Mobile Apps for Dynamic Preference Prediction. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_28
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DOI: https://doi.org/10.1007/978-3-642-37453-1_28
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