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Manually constructing rankings is a tedious ad-hoc process, requiring extensive user effort to evaluate data attribute importance, and often leading to undesirable results. Meanwhile, sophisticated learning-to-rank algorithms are able to leverage large amounts of data to generate high quality rankings automatically. In this work we present RanKit, a novel technology that brings the power of automatic learning-to-rank to the public. RanKit serves as a personal recommender system for building rankings from partial user knowledge in the form of item preferences. A user-friendly rank building interface provides rich input modes for preference specification. Visual feedback on the quality of the learned ranking model is given in real time, empowering the user to guide the underlying learn-to-rank algorithm. Users are actively involved with every step of the rank generation process, developing trust in the model and producing personalized rankings suitable for real-world decision making. In this demonstration, the audience works directly with the RanKit system on public domain datasets ranging from college rankings and economic indicators to movies and sports.

Preference-driven Interactive Ranking System for Personalized Decision Support