Personalizable and Interactive Sequence Recommender System.

Sequence recommender systems assist people in making decisions, such as which product to purchase and what places to visit on vacation. Despite their ubiquity, most sequence recommender systems are black boxes and do not offer justifications for their recommendations or provide user controls for steering the algorithm. In this paper, we design and develop an interactive sequence recommender system (SeRIES) prototype that uses visualizations to explain and justify the recommendations and provides controls so that users may personalize the recommendations. We conducted a user study comparing SeRIES to a black-box system with 12 participants using real visitor trajectory data in Melbourne and show that SeRIES users are more informed about how the recommendations are generated, more confident in following the recommendations, and more engaged in the decision process.