CoSEM: Contextual and semantic embedding for App usage prediction

App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide range of semantic information to predict the app usage; however, they are only effective in certain scenarios and cannot be generalized across different situations. This paper address this problem by developing a model called Contextual and Semantic Embedding model for App Usage Prediction (CoSEM) for app usage prediction that leverages integration of 1) semantic information embedding and 2) contextual information embedding based on historical app usage of individuals. Extensive experiments show that the combination of semantic information and history app usage information enables our model to outperform the baselines on three real-world datasets, achieving an MRR score over 0.55,0.57,0.86 and Hit rate scores of more than 0.71, 0.75, and 0.95, respectively

CoSEM: Contextual and semantic embedding for App usage prediction

App usage prediction is important for smartphone system optimization to enhance user experience. Existing modeling approaches utilize historical app usage logs along with a wide range of semantic information to predict the app usage; however, they are only effective in certain scenarios and cannot be generalized across different situations. This paper address this problem by developing a model called Contextual and Semantic Embedding model for App Usage Prediction (CoSEM) for app usage prediction that leverages integration of 1) semantic information embedding and 2) contextual information embedding based on historical app usage of individuals. Extensive experiments show that the combination of semantic information and history app usage information enables our model to outperform the baselines on three real-world datasets, achieving an MRR score over 0.55,0.57,0.86 and Hit rate scores of more than 0.71, 0.75, and 0.95, respectively

App usage on-the-move: Context- and commute-aware next app prediction

The proliferation of digital devices and connectivity enables people to work anywhere, anytime, even while they are on the move. While mobile applications have become pervasive, an excessive amount of mobile applications have been installed on mobile devices. Nowadays, commuting takes a large proportion of daily human life, but studies show that searching for the desired apps while commuting can decrease productivity significantly and sometimes even cause safety issues. Although app usage behaviour has been studied for general situations, little to no study considers the commuting context as vital information. Existing models for app usage prediction cannot be easily generalised across all commuting contexts due to: (1) continuous change in user locations; and (2) limitation of necessary contextual information (i.e., lack of knowledge to identify which contextual information is necessary for different commuting situations. We aim to address these challenges by extracting essential contextual information for on-commute app usage prediction. Using the extracted features, we propose AppUsageOTM, a practical statistical machine learning framework to predict both destination amenity and utilise the inferred destination to contextualise the app usage prediction with travelling purposes as crucial information. We evaluate our framework in terms of accuracy, which shows the feasibility of our work. Using a real-world mobile and app usage behaviour dataset with more than 12,495 trajectory records and more than 1046 mobile applications logged, AppUsageOTM significantly outperformed all baseline models, achieving Accuracy@k 46.4%@1, 66.4%@5, and 75.9%@10

App usage on-the-move: Context- and commute-aware next app prediction

The proliferation of digital devices and connectivity enables people to work anywhere, anytime, even while they are on the move. While mobile applications have become pervasive, an excessive amount of mobile applications have been installed on mobile devices. Nowadays, commuting takes a large proportion of daily human life, but studies show that searching for the desired apps while commuting can decrease productivity significantly and sometimes even cause safety issues. Although app usage behaviour has been studied for general situations, little to no study considers the commuting context as vital information. Existing models for app usage prediction cannot be easily generalised across all commuting contexts due to: (1) continuous change in user locations; and (2) limitation of necessary contextual information (i.e., lack of knowledge to identify which contextual information is necessary for different commuting situations. We aim to address these challenges by extracting essential contextual information for on-commute app usage prediction. Using the extracted features, we propose AppUsageOTM, a practical statistical machine learning framework to predict both destination amenity and utilise the inferred destination to contextualise the app usage prediction with travelling purposes as crucial information. We evaluate our framework in terms of accuracy, which shows the feasibility of our work. Using a real-world mobile and app usage behaviour dataset with more than 12,495 trajectory records and more than 1046 mobile applications logged, AppUsageOTM significantly outperformed all baseline models, achieving Accuracy@k 46.4%@1, 66.4%@5, and 75.9%@10

Intelligent task recognition: Towards enabling productivity assistance in daily life

We introduce the novel research problem of task recognition in daily life. We recognize tasks such as project management, planning, meal-breaks, communication, documentation, and family care. We capture Cyber, Physical, and Social (CPS) activities of 17 participants over four weeks using device-based sensing, app activity logging, and an experience sampling methodology. Our cohort includes students, casual workers, and professionals, forming the first real-world context-rich task behaviour dataset. We model CPS activities across different task categories, results highlight the importance of considering the CPS feature sets in modelling, especially work-related tasks

Imagining future digital assistants at work: A study of task management needs

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs using data from a user study of 40 workers over a four-week period. Our qualitative analysis confirms existing research and generates new insight on the role of DAs in managing people's time, tasks, and information. Placing these insights in relation to quantitative analysis of self-reported task data, we highlight how different occupation roles require DAs to take varied approaches to these domains and the effect of task characteristics on the imagined features. Our findings have implications for the design of future DAs in work settings and we offer some recommendations for reduction to practice

Imagining future digital assistants at work: A study of task management needs

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs using data from a user study of 40 workers over a four-week period. Our qualitative analysis confirms existing research and generates new insight on the role of DAs in managing people's time, tasks, and information. Placing these insights in relation to quantitative analysis of self-reported task data, we highlight how different occupation roles require DAs to take varied approaches to these domains and the effect of task characteristics on the imagined features. Our findings have implications for the design of future DAs in work settings and we offer some recommendations for reduction to practice