Demographic Information Prediction: A Portrait of Smartphone Application Users

Qin, Zhen; Wang, Yilei; Cheng, Hongrong; Zhou, Yingjie; Sheng, Zhengguo; Leung, Victor C. M.

doi:10.1109/tetc.2016.2570603

Cited by 46 publications

(26 citation statements)

References 19 publications

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“…The performance of prediction is evaluated by Accuracy (Acc), Precision (Prec), Recall (Rec) and F1 value (F1) (Qin et al, 2017). We conduct a 5-fold cross-validation and calculate the four performance metrics.…”

Section: Experiments Resultsmentioning

confidence: 99%

Inferring Social-Demographics of Travellers based on Smart Card Data

Zhang

Cheng

2018

Proceedings of the 2nd International Conference on Advanced Research Methods and Analytics (CARMA 2018)

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Section: Experiments Resultsmentioning

confidence: 99%

Inferring Social-Demographics of Travellers based on Smart Card Data

Zhang

Cheng

2018

Proceedings of the 2nd International Conference on Advanced Research Methods and Analytics (CARMA 2018)

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“…The standard models include Random Forest (RF), Logistic Regression Classifier (LR), Naïve Bayes (NB), and Multi-Layer Perceptron (MLP). These models are selected as the baselines because each has been widely adopted for demographic prediction in existing literature [8], [42]- [44]. To verify the effectiveness of the modification on the configuration, the performance of CNN, multi-channel CNN without Table IV.…”

Section: ) the Sensitivity And Effectiveness Of Temporal Profilesmentioning

confidence: 99%

A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data

Zhang

Cheng

2020

IEEE Trans. Intell. Transport. Syst.

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Understanding the employment status of passengers in public transit systems is significant for transport operators in many real applications such as forecasting travel demand and providing personalized transportation service. This paper develops a deep learning approach to infer a passenger's employment status by using smart card data (SCD) with a household survey. This paper first extracts an individual passenger's weekly travel patterns in different travel modes from the raw SCD as a three-dimensional image. A deep learning architecture, called a thresholding multi-channel convolutional neural network, was developed to predict an individual's employment status. The approach proposed here solves two critical problems of using the SCD for employment status studies. First, it automatically incorporates learning temporal features in different travel modes without the need for handcrafted travel feature design. Second, it considers the class-imbalance problem by leveraging the ensemble of oversampling and thresholding techniques. By applying our approach to a real dataset collected from the metropolitan area of London, U.K., about 72% of passengers were correctly categorized into six types of employment statuses. The promising results show the tight correlation between temporal travel behavior, mode choice, and social-demographic roles. To the best of our knowledge, this is the first paper to infer employment status by using the SCD.Index Terms-Deep learning, employment status inference, travel mode choice, smart card data, temporal travel behavior.

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“…There have been some studies using smartphone apps to infer user personal information. For example, demographic attributes (e.g., gender, region and marital status), interests, personality traits and life stages have been learned from app lists installed on smartphones, app installation behaviors (installation, updating and uninstallation) and app usage behaviors (Chittaranjan et al 2011(Chittaranjan et al , 2013Frey et al 2015Frey et al , 2017Jesdabodi and Maalej 2015;Malmi and Weber 2016;Qin et al 2016;Rivron et al 2016;Seneviratne et al 2015;Tu et al 2019;Wang et al 2015Wang et al , 2018Xu et al 2011Xu et al , 2016bZhao et al 2016Zhao et al , 2017aZhao et al , b, c, 2018Zhao et al , 2019bLi et al 2015a;Mo et al 2012;Brdar et al 2012;Ying et al 2012;Andone et al 2016;Peltonen et al 2018;Zou et al 2013;Yu et al 2018;Ouyang et al 2018;Wang et al 2019;Böhmer et al 2011;Liu et al 2018). In this section, we will review the related work in three aspects: inferring demographics, explaining personality, and discovering life patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Apps on smartphones were used to infer users' demographic attributes (Seneviratne et al 2015;Xu et al 2016b;Zhao et al 2017a;Qin et al 2016;Malmi and Weber 2016;Wang et al 2015). For example, Seneviratne et al inferred about 200 users' gender from their installed app lists, with an accuracy around 70% (Seneviratne et al 2015).…”

Section: Inferring Demographicsmentioning

confidence: 99%

Investigating smartphone user differences in their application usage behaviors: an empirical study

Zhao

et al. 2019

CCF Trans. Pervasive Comp. Interact.

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Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. Users with different attributes may have different needs, making it natural for their app usage behaviors to be different. The differences in app usage behaviors among users make it possible to infer their attributes. Knowing such differences could help improve mobile user experiences by enhancing smart services and devices. In this paper, we present an empirical study of investigating smartphone user differences on a large-scale dataset of recently used app lists from 106,672 Android users from China. We first investigate the user differences in app usage behaviors with respect to their attributes (gender, age, and income level). We find significant differences in app usage frequency, app usage with time context and functions. We then extract corresponding features from app usage records to infer the attributes of each user, and investigate the predictive ability of individual features and combinations of different individual features. We achieve the accuracy of 83.29% for gender, 69.94% for age (four age ranges) and 71.43% for income level (three income levels) with the best set of features, respectively. Finally, we discuss the implications of our findings and the limitations of this work.

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Demographic Information Prediction: A Portrait of Smartphone Application Users

Cited by 46 publications

References 19 publications

Inferring Social-Demographics of Travellers based on Smart Card Data

Inferring Social-Demographics of Travellers based on Smart Card Data

A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data

Investigating smartphone user differences in their application usage behaviors: an empirical study

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