CoSEM: Contextual and Semantic Embedding for App Usage Prediction

Publication Year: 2021 Publication Type : JournalArticle


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.


@inproceedings{khaokaew2021cosem, title={CoSEM: Contextual and Semantic Embedding for App Usage Prediction},
    author={Khaokaew, Yonchanok and Rahaman, Mohammad Saiedur and White, Ryen W and Salim, Flora D},
    booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},


Related Publications

RUP: Large Room Utilisation Prediction with carbon dioxide sensor
Type : JournalArticle
Show More
A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO 2 Sensor Data
Type : JournalArticle
Show More
Topical Event Detection on Twitter
Type : ConferenceProceeding
Show More

© 2021 Flora Salim - CRUISE Research Group.