1 rich Libsyn https://www.libsyn.com 90 600 In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich interviews Dr. Kunal Mukherjee, a postdoctoral research associate at Virginia Tech, about the paper &quot;Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations&quot; The discussion explores how the post-COVID real estate landscape has created a need for better recommendation systems that can introduce home buyers to emerging neighborhoods they might not know about. &amp;nbsp;Dr. Mukherjee, explains how his team developed a graph neural network approach that not only recommends properties but provides human-interpretable explanations for why certain regions are suggested. The conversation covers the advantages of using graph-based models over traditional recommendation systems, the importance of regional context in real estate features, and how co-click data from similar users can create more effective recommendations. Key topics include the distinction between model developer explanations and end-user explanations, the challenges of feature perturbation in recommendation systems, and how graph neural networks can discover novel pathways to emerging real estate markets that traditional models might miss. Data Skeptic https://dataskeptic.com <iframe title="Libsyn Player" style="border: none" src="//html5-player.libsyn.com/embed/episode/id/38317000/height/90/theme/custom/thumbnail/yes/direction/forward/render-playlist/no/custom-color/88AA3C/" height="90" width="600" scrolling="no" allowfullscreen webkitallowfullscreen mozallowfullscreen oallowfullscreen msallowfullscreen></iframe> https://assets.libsyn.com/secure/content/196478960