Title
App2Vec: Context-Aware Application Usage Prediction
Abstract
AbstractBoth app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when, where, and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.
Year
DOI
Venue
2021
10.1145/3451396
ACM Transactions on Knowledge Discovery from Data
Keywords
DocType
Volume
App usage, representation learning, graphic models, spatio-temporal context
Journal
15
Issue
ISSN
Citations 
6
1556-4681
1
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Huandong Wang111414.20
Yong Li22972218.82
Mu Du310.34
Zhenhui Li410.34
Depeng Jin52177154.29