{"version":1,"type":"rich","provider_name":"Libsyn","provider_url":"https:\/\/www.libsyn.com","height":90,"width":600,"title":"Interpretable Real Estate Recommendations","description":"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 "Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations" 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.  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. ","author_name":"Data Skeptic","author_url":"https:\/\/dataskeptic.com","html":"