Content recommender systems often rely on modeling users’ past behavioral data to provide personalized recommendations - a practice that works well for suggesting more of the same and for media that require little time investment from users, such as music tracks. However, this approach can be further optimized for media where the user investment is higher, such as podcasts, because there is a broader space of user goals that might not be captured by the implicit signals of their past behavior. Allowing users to directly specify their goals might help narrow the space of possible recommendations. Thus, in this paper, we explore how we can enable goal-focused exploration in recommender systems by leveraging explicit input from users about their personal goals. Using podcast consumption as an example use-case, and informed by a large-scale survey (N=68k), we developed GoalPods, an interactive prototype that allows users to set personal goals and build playlists of podcast episode recommendations to meet those goals. We evaluated GoalPods with 14 participants where participants set a goal and spent a week listening to the episode playlist created for that goal. From the study, we identified two types of user goals: low-involvement (e.g. “combat boredom”) and high-involvement (e.g. “learn something new”) goals. Users found it easy to identify relevant recommendations for low-involvement goals, but they needed more structure and support to set high-involvement goals. By anchoring users on their personal goals to explore recommendations, GoalPods (and goal-focused podcast consumption) led to insightful content discovery outside the users’ filter bubbles. Based on our findings, we discuss opportunities for designing recommender systems that guide exploration via interactive goal-setting as well as implications for providing better recommendations by accounting for users’ personal goals.

Recommender systems are ubiquitous and influence the information we consume daily by helping us navigate vast catalogs of information like music databases. However, their linear approach of surfacing content in ranked lists limits their ability to help us grow and understand our personal preferences. In this paper, we study how we can better support users in exploring a novel space, specifically focusing on music genres. Informed by interviews with expert music listeners, we developed TastePaths: an interactive web tool that helps users explore an overview of the genre-space via a graph of connected artists. We conducted a comparative user study with 16 participants where each of them used a personalized version of TastePaths (built with a set of artists the user listens to frequently) and a non-personalized one (based on a set of the most popular artists in a genre). We find that participants employed various strategies to explore the space. Overall, they greatly preferred the personalized version as it helped anchor their exploration and provided recommendations that were more compatible with their personal taste. In addition to that, TastePaths helped participants specify and articulate their interest in the genre and gave them a better understanding of the system’s organization of music. Based on our findings, we discuss opportunities and challenges for incorporating more control and expressive feedback in recommendation systems to help users explore spaces beyond their immediate interests and improve these systems’ underlying algorithms.

The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E’s directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system.

Self-experiments allow people to explore what behavioral changes lead to improved health and wellness. However, it is challenging to run such experiments in a scientifically valid way that is also flexible and able to accommodate the realities of daily life. We present a set of design principles for guided self-experiments that aim to lower this barrier to self-experimentation. We demonstrate the value of the principles by implementing them in SleepBandits, an integrated system that includes a smartphone application for sleep experiments. SleepBandits guides users through the steps of a single-case experiment, automatically collecting data from the built-in sensors and user input and calculating and presenting results in real-time. We released SleepBandits to the Google Play Store and people voluntarily downloaded and used it. Based on the data from 365 active users from this in-the-wild study, we discuss opportunities and challenges with the design principles and the SleepBandits system.

Much of the existing research literature on email use focuses on productivity or work settings. However, personal use of email has rarely been studied in depth. With the growth of messaging platforms being used for an increasing amount of personal communication, yet email use remaining high, we were interested in learning what Americans are using email for in their daily lives in 2016. To explore this topic, we use qualitative data from over 150 interviews with personal email users as well as quantitative data from several larger survey-based studies. We will show that personal email use is very different from what has been previously studied by workplace researchers and that daily use is largely focused on receiving and viewing B2C messages such as coupons, deals, receipts, and event notifications with personal communication over email diminished to a rarer, less-than-daily occurrence. We discuss the implications of this for the design of email and communications clients and present a design and prototype for an application that seeks to support these more frequent uses of consumer email.

We present SleepCoacher, an integrated system implementing a framework for effective self-experiments. SleepCoacher automates the cycle of single-case experiments by collecting raw mobile sensor data and generating personalized, data-driven sleep recommendations based on a collection of template recommendations created with input from clinicians. The system guides users through iterative short experiments to test the effect of recommendations on their sleep. We evaluate SleepCoacher in two studies, measuring the effect of recommendations on the frequency of awakenings, self-reported restfulness, and sleep onset latency, concluding that it is effective: participant sleep improves as adherence with SleepCoacher's recommendations and experiment schedule increases. This approach presents computationally-enhanced interventions leveraging the capacity of a closed feedback loop system, offering a method for scaling guided single-case experiments in real time.