  App usage prediction is important for smartphone system optimization to
enhance user experience. Existing modeling approaches utilize historical app
usage logs along with a wide range of semantic information to predict the app
usage; however, they are only effective in certain scenarios and cannot be
generalized across different situations. This paper address this problem by
developing a model called Contextual and Semantic Embedding model for App Usage
Prediction (CoSEM) for app usage prediction that leverages integration of 1)
semantic information embedding and 2) contextual information embedding based on
historical app usage of individuals. Extensive experiments show that the
combination of semantic information and history app usage information enables
our model to outperform the baselines on three real-world datasets, achieving
an MRR score over 0.55,0.57,0.86 and Hit rate scores of more than 0.71, 0.75,
and 0.95, respectively.
