Cracking the Cold Start Problem

About this episode

This episode explores the technical foundations of recommender systems and addresses the cold start problem with insights from guest Boya Xu.

In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply apply XGBoost to tabular data, recommender systems require sophisticated hybrid approaches that combine multiple techniques. Our guest, Boya Xu, an assistant professor of marketing at Virginia Tech, walks us through a cutting-edge method that integrates three key components: collaborative filtering for dimensionality reduction, embeddings to represent users and items in latent space, and bandit learning to balance exploration and exploitation when deploying new recommendations. Boya shares insights from her research on how recommender systems impact both consumers and content creators across e-commerce and social media platforms. We explore critical challenges like the cold start problem—how to make good recommendations for brand new users—and discuss how her approach uses demographic information to create informative priors that accelerate learning. The conversation also touches on algorithmic fairness, revealing how her method reduces bias between majority and minority (niche…

People in this episode

Host: Kyle Polich

Guest: Boya Xu

Topics covered

• recommender systems
• machine learning
• collaborative filtering
• algorithmic fairness
• cold start problem

Keywords

• recommender systems
• collaborative filtering
• bandit learning
• algorithmic fairness
• cold start problem
• dimensionality reduction
• active learning

Mentioned in this episode

Organizations: Virginia Tech

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