Ph.D Dissertation from the Department of Computer Science (2018)
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 dissertation 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. Event sequence recommender
systems use archives of similar event sequences, such as patient histories or student
academic records, to give users insight into the order and timing of choices, which
are more likely to lead to their desired outcomes.
This dissertation’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 generated
based on choices by people with similar attributes, such as those who looked at this product or watched this movie, the event sequence recommendation approach allows
users to select records that share similar attribute values and start with a similar
event sequence, and then see how different choices of actions and the orders and
times between them might lead to users’ desired outcomes.
This dissertation 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. Results from empirical studies
show that these prototypes can assist users in making action plans and raise users’
confidence in following their plans. It presents case studies in three domains to
demonstrate the effectiveness and safety 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.
This dissertation contributes an analytical workflow, an interactive system,
and design guidelines identified in empirical studies and case studies, opening new
avenues of research in explainable event sequence recommendations based on personal
histories. It enables people to make better decisions for critical life choices
with higher confidence.