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.
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32:57
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32:57
Why Am I Seeing This?
In this episode of Data Skeptic, we explore the challenges of studying social media recommender systems when exposure data isn't accessible. Our guests Sabrina Guidotti, Gregor Donabauer, and Dimitri Ognibene introduce their innovative "recommender neutral user model" for inferring the influence of opaque algorithms.
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49:36
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49:36
Eco-aware GNN Recommenders
In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks for Sustainable Recommendations" and explores how we can measure and reduce the environmental impact of recommender systems without sacrificing performance.
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44:42
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44:42
Networks and Recommender Systems
Kyle reveals the next season's topic will be "Recommender Systems". Asaf shares insights on how network science contributes to the recommender system field.
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17:45
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17:45
Network of Past Guests Collaborations
Kyle and Asaf discuss a project in which we link former guests of the podcast based on their co-authorship of academic papers.
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
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