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Mobi-SAGE: A Sparse Additive Generative Model for Mobile App Recommendation

Hongzhi Yin, Liang Chen, Weiqing Wang, Xingzhong Du, Quoc Viet Hung Nguyen, Xiaofang Zhou
2017 2017 IEEE 33rd International Conference on Data Engineering (ICDE)  
In this paper, we propose a mobile sparse additive generative model (Mobi-SAGE) to recommend Apps by considering both user interests and category-aware user privacy preferences.  ...  With the rapid prevalence of smart mobile devices and the dramatic proliferation of mobile applications (Apps), App recommendation becomes an emergent task that will benefit different stockholders of mobile  ...  THE MOBI-SAGE MODEL To model mobile users' downloading behaviors on App stores, we propose a mobile sparse additive generative model (Mobi-SAGE) based on SAGE model [2] .  ... 
doi:10.1109/icde.2017.43 dblp:conf/icde/YinCWDHZ17 fatcat:w24fw7ffnbafnl5od4aglmmsay

A Recommender System for Mobile Applications of Google Play Store

Ahlam Fuad, Sahar Bayoumi, Hessah Al-Yahya
2020 International Journal of Advanced Computer Science and Applications  
Indeed, there is a critical demand for personalized application recommendations.  ...  Based on the number of installations, the number of reviews, app size, and category, we developed a content-based recommender system that can suggest some apps for users based on what they have searched  ...  Another study [41] introduced a mobile sparse additive generative model (Mobi-SAGE) to recommend apps. They crawled an extensive collection of apps from the 360 App Store in China.  ... 
doi:10.14569/ijacsa.2020.0110906 fatcat:fxdhkoe4vvdzncsqznibhpk4nm