Recommender Systems Origin Story

Where did recommender systems come from, and how do we know when they’re actually working? In part one of Data Skeptic’s three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factorization and modern approaches, while exploring why accuracy alone can’t capture what makes a recommendation useful, surprising, or meaningful.

Guests

Kyle Polich: Kyle is the founder of Data Skeptic, a popular podcast about artificial intelligence, machine learning, and data science. Outside of hosting the show, he runs a boutique consulting group that helps small and medium enterprise companies deploy data driven automated solutions in the cloud.

Santiago de Leon: I am a Spanish American doctoral researcher at Kempelen Institute of Intelligent Technologies in Bratislava, Slovakia and also part of the MSCA Eyes4ICU doctoral network (most prestigious EU grants completely funding a PhD). I have a master’s degree in Health Information Engineering from the University Carlos III of Madrid, with previous research experience in physics, neurobiology, chemistry, and mathematics. I began university level research in high school, as I was selected for a gifted and talented program. At my undergraduate institution, the University of Kentucky, I received the highest merit-based scholarship and graduated with three bachelor degrees in 4 years: mathematics, chemistry, and Spanish. In 2018, I joined two ongoing research groups in Madrid, Spain, one in psychiatry at the Jiménez Díaz Foundation and the other in machine learning at the University Carlos III of Madrid, where I focused on modeling psychiatric patients through their data and was a clinical research head. I have multiple publications spanning the fields of mathematical number theory, psychiatry, and machine learning. My current research focuses on user modeling within recommender systems, understanding how users browse and interact systems with eye tracking data to build interface-aware and gaze-informed recommenders.

Asaf Shapira: The podcaster of NETfrix – The Network Science Podcast. Works as a network analysis consultant. His Master's degree thesis is about ONA and influencers in organizations. Currently he's initiating courses in network analysis in colleges.

Ashmi Banerjee: Ashmi is currently a doctoral researcher at the Chair of Connected Mobility at the Technical University of Munich. Her research focuses on Recommender Systems, specifically in the tourism domain. She graduated with a master's degree in Computer Science in 2019 from the same university and also holds three years of industry experience at different companies across Germany. She is passionate about using technology to automate tedious tasks and is always excited to tackle new technical challenges. Over the past two years, she has delivered over 50 tech talks across 15+ countries on three continents. She was named one of the 100 technologists to watch for 2023 and won the Google Developer Expert Community Award (Rising Star), the 2023 Women Who Code Applaud Her Award (Data Science), and the DevelopHER Awards 2022 (Emerging Talent). As a Google Women Techmakers (WTM) Ambassador and diversity advocate, she is dedicated to closing the gender gap in STEM through her involvement in various women in STEM networks. When not sitting in front of her computer, she can usually be found traveling, collecting passport stamps, and fridge magnets. ✈️

Cory Zechmann: a content curator working in streaming television with 16 years of experience running the music blog "A Silence No Good."

Andreea Iana: I am a postdoctoral researcher in the Data and Web Science Group at the University of Mannheim. I hold a PhD in Recommender Systems from the University of Mannheim, where I was advised advised by Prof. Heiko Paulheim (University of Mannheim) and Prof. Goran Glavaš (University of Würzburg). During my PhD, I was a visting researcher in the WüNLP Group at the University of Würzburg. Before that, I completed my M.Sc. in Business Informatics at the University of Mannheim, and my B.Sc. in Liberal Arts and Sciences from the University College Maastricht, in the Netherlands. I work on advancing responsible and inclusive AI for information access, with a particular focus on multilinguality, cross-lingual IR, and investigating and mitigating algorithmic bias in retrieval and recommendation systems.

Kunal Mukherjee: I am a Postdoctoral Research Associate in the Department of Computer Science at Virginia Tech (Blacksburg, VA), working closely with Dr. Murat Kantarcıoğlu. I received my M.S. and Ph.D. in Computer Science from The University of Texas at Dallas, where I worked in the UTD SysSec Lab with Kangkook Jee and Murat Kantarcıoğlu.

Alberto Carlo Maria Mancino: I am Alberto Carlo Maria Mancino, a post-doctoral researcher in Artificial Intelligence. You can find me at the Politecnico di Bari, Italy. My research focuses on Recommender Systems, particularly in studying knowledge-aware recommenders, graph-based recommenders, and the impact of data characteristics on recommenders' privacy and robustness.

Florian Atzenhofer-Baumgartner: I wear many hats in DiDip (didip.eu), e.g., of data analytics, machine learning, DevOps, for historical document analysis systems. I also coordinate and help with research infrastructure efforts in DHInfra (dhinfra.at), including a specialized GPU cluster for humanities computing. I hold a BEd in German and English, with my thesis on learner corpora; a MA in Digital Humanities, with my thesis on text similarity - both from the University of Graz. I am also a PhD candidate at the Institute of Interactive Systems and Data Science (Technical University of Graz), where I do research on recommender systems and ranking in digital humanities and cultural heritage contexts.