ScreenLife Capture: An open-source and user-friendly framework for collecting screenomes from Android smartphones.

As our interactions with each other become increasingly digitally mediated, there is growing interest in the study of people's digital experiences. To better understand digital experiences, some researchers have proposed the use of screenomes. This involves the collection of sequential high-frequency screenshots which provide detailed objective records of individuals' interaction with screen devices over time. Despite its usefulness, there remains no readily available tool that researchers can use to run their own screenome studies. To fill this gap, we introduce ScreenLife Capture, a user-friendly and open-source software to collect screenomes from smartphones. Using this tool, researchers can set up smartphone screenome studies even with limited programming knowledge and resources. We piloted the tool in an exploratory mixed-method study of 20 college students, collecting over 740,000 screenshots over a 2-week period. We found that smartphone use is highly heterogeneous, characterized by threads of experiences. Using in-depth interviews, we also explored the impact that constant background surveillance of smartphone use had on participants. Participants generally had slight psychological discomfort which fades after a few days, would suspend screen recording for activity perceived to be extremely private, and recounted slight changes in behavior. Implications for future research is discussed.

POI recommendation with queuing time and user interest awareness.

Point-of-interest (POI) recommendation is a challenging problem due to different contextual information and a wide variety of human mobility patterns. Prior studies focus on recommendation that considers user travel spatiotemporal and sequential patterns behaviours. These studies do not pay attention to user personal interests, which is a significant factor for POI recommendation. Besides user interests, queuing time also plays a significant role in affecting user mobility behaviour, e.g., having to queue a long time to enter a POI might reduce visitor's enjoyment. Recently, attention-based recurrent neural networks-based approaches show promising performance in the next POI recommendation task. However, they are limited to single head attention, which can have difficulty in finding the appropriate user mobility behaviours considering complex relationships among POI spatial distances, POI check-in time, user interests and POI queuing times. In this research work, we are the first to consider queuing time and user interest awareness factors for next POI recommendation. We demonstrate how it is non-trivial to recommend a next POI and simultaneously predict its queuing time. To solve this problem, we propose a multi-task, multi-head attention transformer model called TLR-M_UI. The model recommends the next POIs to the target users and predicts queuing time to access the POIs simultaneously by considering user mobility behaviours. The proposed model utilises POIs description-based user personal interest that can also solve the new categorical POI cold start problem. Extensive experiments on six real-world datasets show that the proposed models outperform the state-of-the-art baseline approaches in terms of precision, recall, and F1-score evaluation metrics. The model also predicts and minimizes the queuing time. For the reproducibility of the proposed model, we have publicly shared our implementation code at GitHub (https://github.com/sajalhalder/TLR-M_UI).