EventAction

By Fan Du, Catherine Plaisant, Neil T. Spring, Kenyon Crowley, Ben Shneiderman. 2019

17 citations recorded by the source corpus.

People use recommender systems to improve their decisions; for example, item recommender systems help them find films to watch or books to buy. Despite the ubiquity of item recommender systems, they can be improved by giving users greater transparency and control. This article develops and assesses interactive strategies for transparency and control, as applied to event sequence recommender systems, which provide guidance in critical life choices such as medical treatments, careers decisions, and educational course selections. This article’s main contribution is the use of both record attributes and temporal event information as features to identify similar records and provide appropriate recommendations. While traditional item recommendations are based on choices by people with similar attributes, such as those who looked at this product or watched this movie, our event sequence recommendation approach allows users to select records that share similar attribute values and start with a similar event sequence. Then users see how different choices of actions and the orders and times between them might lead to users’ desired outcomes. This paper applies a visual analytics approach to present and explain recommendations of event sequences. It presents a workflow for event sequence recommendation that is implemented in EventAction and reports on three case studies in two domains to illustrate the use of generating event sequence recommendations based on personal histories. It also offers design guidelines for the construction of user interfaces for event sequence recommendation and discusses ethical issues in dealing with personal histories. A demo video of EventAction is available at https://hcil.umd.edu/eventaction.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Explainable Recommendation: A Survey and New Perspectives (2018, background): “For instance, He et al. (2015) introduced a tripartite graph ranking algorithm for explainable recommendation of electronics products; Chen et al.”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “]. The interpretability element in XAI visual analytic systems increase data experts’ ability to compare multiple models [5] and select the right model for the targeted data. As an example, Du et al. [51] present EventAction, an event sequence recommendation approach that allows the users to interac”