RecDelta: An Interactive Dashboard on Top-k Recommendation for Cross-model Evaluation

Abstract

In this demonstration, we present RecDelta, an interactive tool for the cross-model evaluation of top-k recommendation. RecDelta is a web-based information system where people visually compare the performance of various recommendation algorithms and their recommended items. In the proposed system, we visualize the distribution of the δscores between algorithms - a distance metric measuring the intersection between recommendation lists. Such visualization allows for rapid identification of users for whom the items recommended by different algorithms diverge or vice versa; then, one can further select the desired user to present the relationship between recommended items and his/her historical behavior. RecDelta benefits both academics and practitioners by enhancing model explainability as they develop recommendation algorithms with their newly gained insights. Note that while the system is now online at https: //cfda.csie.org/recdelta, we also provide a video recording at https: //tinyurl.com/RecDelta to introduce the concept and the usage of our system.