BBL: EventAction: Visual Analytics for Temporal Event Sequence Recommendation
HCIL (2105 Hornbake, South Wing)
EventAction: Visual Analytics for Temporal Event Sequence Recommendation
Recommender systems are being widely used to assist people in making decisions, for example, recommending films to watch or books to buy. Despite its ubiquity, the problem of presenting the recommendations of temporal event sequences has not been studied. We propose EventAction, which to our knowledge, is the first attempt at a prescriptive analytics interface designed to present and explain recommendations of temporal event sequences. EventAction provides a visual analytics approach to (1) identify similar records, (2) explore potential outcomes, (3) review recommended temporal event sequences that might help achieve the users’ goals, and (4) interactively assist users as they define a personalized action plan associated with a probability of success. Following the design study framework, we designed and deployed EventAction in the context of student advising and reported on the evaluation with a student review manager and three graduate students.
Fan Du is a computer science Ph.D. student at the University of Maryland, College Park. He works as a research assistant with Prof. Ben Shneiderman and Dr. Catherine Plaisant. His research focuses on data visualization and human-computer interaction, especially on analyzing healthcare data and user activity logs