Abstract
Many app recommendation models have been proposed to provide mobile users with the apps that meet their individual needs. However, three main drawbacks limit their performance: (1) Neglect the dynamic change of user preferences in a short time; (2) Singly use either the machining learning or the deep learning method, which cannot learn discrete features or continuous features very well; (3) Directly deal with all apps without considering their hierarchical features. To overcome the above drawbacks, this paper proposes a Dynamic behavior-based age Hierarchy Model (DHM for short). To be specific, we integrate the Boosting Tree and Neural Network, combine the static data as basis and the dynamic behaviors as refinement, and update dynamic behaviors in time to improve the accuracy of personalized app recommendation. Then, this paper proposes a User Hierarchy based personalized App recommendation Model (UHAM for short), it exploits the user attribute layering method to make hierarchical recommendation for users in different age groups, which further enhance the efficiency. We conduct extensive experiments with a real app dataset, and the results validate the effectiveness of our model.
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Acknowledgment
This research was supported by NSFC grant 61632009, Guangdong Provincial NSF Grant 2017A030308006, the science and technology program of Changsha city kq2004017 and Open project of Zhejiang Lab 2019KE0AB02.
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Qu, T., Jiang, W., Liu, D., Wang, G. (2021). Deep Hierarchical App Recommendation with Dynamic Behaviors. In: Thampi, S.M., Wang, G., Rawat, D.B., Ko, R., Fan, CI. (eds) Security in Computing and Communications. SSCC 2020. Communications in Computer and Information Science, vol 1364. Springer, Singapore. https://doi.org/10.1007/978-981-16-0422-5_5
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