Predicting Smartphone App Usage with Recurrent Neural Networks

Xu, Shijian; Li, Wenzhong; Zhang, Xiao; Gao, Songcheng; Tong, Zhan; Zhao, Yuxin; Zhu, Weiwei; Sun, Tianzi

doi:10.1007/978-3-319-94268-1_44

Cited by 12 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, temporal-sequence information was not properly utilized in these works and few of them made use of historical data and context information for prediction jointly. In our previous study (Xu et al 2018), we incorporated long-term dependency and contextual features to develop a LSTM model for app prediction. In this paper, we further extend the model to solve the two subproblems of T app prediction and top-K app recommendation, which are presented in the later sections.…”

Section: App Usage Predictionmentioning

confidence: 99%

Predicting and Recommending the next Smartphone Apps based on Recurrent Neural Network

Zhang

et al. 2020

CCF Trans. Pervasive Comp. Interact.

Self Cite

View full text Add to dashboard Cite

The popularity of smartphones has witnessed the rapid growth of the number of mobile applications. Nowadays, there are millions of applications available, and at the same time, many applications are already installed on people's smartphones. Installing numerous apps will cause some troubles in finding the specific apps promptly. Hence it is necessary to predict the next app(s) to be used in a short term and launching them as shortcuts, which will make the smartphone system more efficient and user-friendly. In this paper, we pay attention to two subproblems that are related to the app usage prediction. One is the T app prediction problem that focuses on predicting a set of apps that will be used in a time interval. The other is the Top-K app recommendation problem that focuses on recommending the K most probable APPs to be used next. In order to solve these problems, we propose a generic prediction model based on Long Short-term Memory (LSTM), which is an enhancement of the recurrent neural network (RNN) model. The proposed model converts the temporal-sequence dependency and contextual information into a unified feature representation for next app prediction. We implement the model in the Android platform. Extensive experiments based on real collected dataset demonstrate that the proposed LSTM model outperforms the baselines for app usage prediction, and achieves high accuracy for app recommendation.

show abstract

Section: App Usage Predictionmentioning

confidence: 99%

Predicting and Recommending the next Smartphone Apps based on Recurrent Neural Network

Zhang

et al. 2020

CCF Trans. Pervasive Comp. Interact.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, with the development of Neural Networks in recent years, some works began to adopt this method. Xu et al [15] proposed a LSTM based multi-label classification model for App usage prediction, which explores the temporal-sequence dependency and contextual information as features for prediction. Xu et al [16] revealed that the similarity of users' preferences is related to the similarity in their App usage context patterns.…”

Section: App Usage Predictionmentioning

confidence: 99%

Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding

Zhou

Liu

2020

Future Internet

View full text Add to dashboard Cite

Smartphones and applications have become widespread more and more. Thus, using the hardware and software of users’ mobile phones, we can get a large amount of personal data, in which a large part is about the user’s application usage patterns. By transforming and extracting these data, we can get user preferences, and provide personalized services and improve the experience for users. In a detailed way, studying application usage pattern benefits a variety of advantages such as precise bandwidth allocation, App launch acceleration, etc. However, the first thing to achieve the above advantages is to predict the next application accurately. In this paper, we propose AHNEAP, a novel network embedding based framework for predicting the next App to be used by characterizing the context information before one specific App being launched. AHNEAP transforms the historical App usage records in physical spaces to a large attributed heterogeneous network which contains three node types, three edges, and several attributes like App type, the day of the week. Then, the representation learning process is conducted. Finally, the App usage prediction problem was defined as a link prediction problem, realized by a simple neural network. Experiments on the LiveLab project dataset demonstrate the effectiveness of our framework which outperforms the three baseline methods for each tested user.

show abstract

“…Modeling the latent relations between apps is of great importance because while people use few apps on a regular basis, they tend to switch between apps multiple times [18]. In fact, previous studies have tried to address app usage prediction by modeling personal and contextual features [10], exploiting context-dependency of app usage patterns [35], sequential order of apps [59] and collaborative models [56].…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [56] modeled the apps following the idea of collaborative filtering, proposing a context-aware collaborative filtering model to unload and pre-load apps. Xu et al [59] modeled the sequential app usage using recurrent networks. Zhao et al [63] proposed the AppUsage2Vec model, inspired by doc2vec.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants

Aliannejadi¹,

Zamani²,

Crestani³

et al. 2021

Preprint

View full text Add to dashboard Cite

Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more pervasive in users' lives. This paper addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation. The former is the key component of a unified mobile search system: a system that addresses the users' information needs for all the apps installed on their devices with a unified mode of access. The latter, instead, predicts the next apps that the users would want to launch. Here we focus on context-aware models to leverage the rich contextual information available to mobile devices. We design an in situ study to collect thousands of mobile queries enriched with mobile sensor data (now publicly available for research purposes). With the aid of this dataset, we study the user behavior in the context of these tasks and propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users. We study several state-of-the-art models and show that the proposed models significantly outperform the baselines.

show abstract

Predicting Smartphone App Usage with Recurrent Neural Networks

Cited by 12 publications

References 15 publications

Predicting and Recommending the next Smartphone Apps based on Recurrent Neural Network

Predicting and Recommending the next Smartphone Apps based on Recurrent Neural Network

Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding

Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants

Contact Info

Product

Resources

About