Notifications are one of the most essential features of smartphones. App developers and researchers typically want to reach their audience at opportune times. We analyzed almost 10 million notifications collected from 922 users. Most notifications were logged in the late morning and late afternoon. Between 8 pm and 11 pm, the interaction delay (time between … Read more
How can mobile sensing, using the smartphone’s sensors for recording and storing data, help in personality science? Read our comprehensive book chapter on this topic. We cover an overview of related work and show the differences in researching personality traits vs. personality states with mobile sensing. We showcase a concrete mobile-sensing application and provide a … Read more
Today, I gave a presentation at an IEEE EMBS (Engineering in Medicine and Biology Society) event. My talk was about Predicting Adherence to Ecological Momentary Assessments, and I presented our findings about the predictability of users of a smartphone app filling out questionnaires. Based on smartphone sensor data, we utilized machine learning to try to … Read more
Next app prediction can help enhance user interface design, pre-loading of apps, and network optimizations. Prior work has explored this topic, utilizing multiple different approaches but challenges like the user cold-start problem, data sparsity, and privacy concerns related to contextual data like location histories, persist. The user cold-start problem occurs when a user has recently … Read more
My PhD thesis (full title: Integrating Psychoinformatics with Ubiquitous Social Networking: Advanced Mobile-Sensing Concepts and Applications) was published as a book with Springer. You can read a preview of the book here. You can buy from Springer directly, or get it via Amazon (for example, US, DE, JP).
Imagine getting recommendations, for example, for music or TV shows, based on similar people you pass by on the street. No server backend needed, all via device-to-device communication. We designed and implemented such a system. You can find our full article published in IEEE Access here (PDF) and the code on GitHub. We build the … Read more
Expanding on our earlier work on privacy in mobile sensing/data collection apps, we evaluated the data we collected with TYDR. Our experimental evaluation based on the first two month of data collected with TYDR shows evidence that our users accept our proposed privacy model. Based on data about granting TYDR all or no Android system … Read more
Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user’s personality. As filling out personality questionnaires is tedious, we propose the prediction of the user’s personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone … Read more
While existing social networking services tend to connect people who know each other, people show a desire to also connect to yet unknown people in physical proximity. Existing research shows that people tend to connect to similar people. Utilizing technology in order to stimulate human interaction between strangers, we consider the scenario of two strangers … Read more
Two of the defining elements of Social Networking Services are the social profile, containing information about the user, and the social graph, containing information about the connections between users. Social Networking Services are used to connect to known people as well as to discover new contacts. Current friend recommendation mechanisms typically utilize the social graph. … Read more
