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

CoSEM: Contextual and Semantic Embedding for App Usage Prediction