Are You Killing Time? Predicting Smartphone Users’ Time-killing Moments via Fusion of Smartphone Sensor Data and Screenshots

要旨

Time-killing on smartphones has become a pervasive activity, and could be opportune for delivering content to their users. This research is believed to be the first attempt at time-killing detection, which leverages the fusion of phone-sensor and screenshot data. We collected nearly one million user-annotated screenshots from 36 Android users. Using this dataset, we built a deep-learning fusion model, which achieved a precision of 0.83 and an AUROC of 0.72. We further employed a two-stage clustering approach to separate users into four groups according to the patterns of their phone-usage behaviors, and then built a fusion model for each group. The performance of the four models, though diverse, yielded better average precision of 0.87 and AUROC of 0.76, and was superior to that of the general/unified model shared among all users. We investigated and discussed the features of the four time-killing behavior clusters that explain why the models’ performance differ.

著者
Yu-Chun Chen
National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Yu-Jen Lee
National Yang Ming Chiao Tung University , Hsinchu, Taiwan
Kuei-Chun Kao
National Yang Ming Chiao Tung Univeristy, Hsinchu, Taiwan
Jie Tsai
National Yang Ming Chiao Tung University, Hsinchu, Taiwan
En-Chi Liang
National Yang Ming Chiao Tung University, Hsinchu,Taiwan, Taiwan
Wei-Chen Chiu
National Chiao Tung University, Hsinchu City, Taiwan
Faye Shih
Bryn Mawr College, Bryn Mawr, Pennsylvania, United States
Yung-Ju Chang
National Yang Ming Chiao Tung University, Hsinchu, Taiwan
論文URL

https://doi.org/10.1145/3544548.3580689

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Smartphones and Notifications

Hall G2
6 件の発表
2023-04-26 01:35:00
2023-04-26 03:00:00