IS2R: A System for Refining Reverse Top-k Queries

Qing Liu, Yunjun Gao, Linlin Zhou, Gang Chen

Abstract: We develop IS2R, an efficient interactive system for refining reverse top-k queries, to eliminate unexpected query results including (i) the absence of expected objects, (ii) the presence of unexpected objects, and (iii) the empty query result. The IS2R returns the refinement suggestions with the minimal costs based on penalty models. In this demonstration (available at https://youtu.be/GnSm4T9Uslk), we show different scenarios on how IS2R can be used to refine the original reverse top-k query, and verify its effectiveness and efficiency.