Predicting Mobile App Usage With Context-Aware Dynamic Hypergraphs

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zihan Huang;Tong Li;Xing Wang;Kexin Yang;Chao Deng;Junlan Feng;Yong Li
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引用次数: 0

Abstract

App usage prediction aims to predict the next app most likely to be used based on historical behaviors, which is beneficial for smartphone system optimization, such as system resource management, battery energy optimization, and user experience enhancement. Existing studies have treated it as a simple time series prediction problem and overlooked the sessionization characteristic of mobile app usage, i.e., neglecting the intent context in which the user interacts with apps. In this paper, we explore the context of user intents and incorporate app sessionization features into prediction models to improve prediction accuracy. Specifically, we first extract the semantic meaning of spatio-temporal contextual information of app usage by constructing an urban knowledge graph. Second, we devise a hypergraph-based embedding model to extract the hyper-relations of intra-session apps. Third, we utilize a self-attention mechanism to fuse intra-session apps’ representations and combine spatio-temporal contextual embedding to form the session representation. We further leverage a transformer for inter-session intent transition modeling to extract users’ dynamic intent (i.e., the semantic meaning of sessions) for app usage. Finally, we jointly fuse dynamic intent and recently used app features using the MLP model for the prediction. The novelty of our method is that we are the first to leverage dynamic hypergraphs for modeling sessionization features, and we model both inter-session and intra-session relations. We evaluate our model based on two real-world datasets collected in Shanghai and Nanchang. In terms of prediction accuracy, mean reciprocal rank, and normalized discounted cumulative gain, our proposed framework outperforms state-of-the-art baselines by more than 30% in the Shanghai dataset and 20% in the Nanchang dataset, respectively.
使用上下文感知的动态超图预测移动应用的使用情况
应用使用预测旨在根据历史行为预测下一个最有可能被使用的应用,有利于智能手机系统优化,如系统资源管理、电池能量优化、用户体验增强等。现有研究将其视为一个简单的时间序列预测问题,忽略了移动应用使用的会话化特征,即忽略了用户与应用交互的意图语境。在本文中,我们探索了用户意图的上下文,并将应用会话化特征纳入预测模型以提高预测精度。具体而言,我们首先通过构建城市知识图谱提取应用使用时空语境信息的语义。其次,我们设计了一个基于超图的嵌入模型来提取会话内应用的超关系。第三,利用自关注机制融合会话内应用的表征,并结合时空上下文嵌入形成会话表征。我们进一步利用一个转换器进行会话间意图转换建模,以提取用户的动态意图(即会话的语义含义),以供应用程序使用。最后,我们使用MLP模型将动态意图和最近使用的应用功能结合起来进行预测。我们方法的新颖之处在于,我们是第一个利用动态超图对会话化特征进行建模的方法,并且我们对会话间和会话内的关系都进行了建模。我们基于在上海和南昌收集的两个真实数据集来评估我们的模型。在预测精度、平均倒数秩和标准化贴现累积增益方面,我们提出的框架在上海数据集和南昌数据集分别比最先进的基线高出30%和20%以上。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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