Exploring Personalization in Spotify and Netflix Recommendations

In episode 19 of Recsperts, we welcome Himan Abdollahpouri who is an Applied Research Scientist for Personalization & Machine Learning at Spotify. We discuss the role of popularity bias in recommender systems which was the dissertation topic of Himan. We talk about multi-objective and multi-stakeholder recommender systems as well as the challenges of music and podcast streaming personalization at Spotify.

In our interview, Himan walks us through popularity bias as the main cause of unfair recommendations for multiple stakeholders. We discuss the consumer- and provider-side implications and how to evaluate popularity bias. Not the sheer existence of popularity bias is the major problem, but its propagation in various collaborative filtering algorithms. But we also learn how to counteract by debiasing the data, the model itself, or it's output. We also hear more about the relationship between multi-objective and multi-stakeholder recommender systems.

At the end of the episode, Himan also shares the influence of popularity bias in music and podcast streaming at Spotify as well as how calibration helps to better cater content to users' preferences.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.
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• (00:00) - Introduction
• (04:43) - About Himan Abdollahpouri
• (15:23) - What is Popularity Bias and why is it important?
• (25:05) - Effect of Popularity Bias in Collaborative Filtering
• (30:30) - Individual Sensitivity towards Popularity
• (36:25) - Introduction to Bias Mitigation
• (53:16) - Content for Bias Mitigation
• (56:53) - Evaluating Popularity Bias
• (01:05:01) - Popularity Bias in Music and Podcast Streaming
• (01:08:04) - Multi-Objective Recommender Systems
• (01:16:13) - Multi-Stakeholder Recommender Systems
• (01:18:38) - Recommendation Challenges at Spotify
• (01:35:16) - Closing Remarks

• Himan Abdollahpouri on LinkedIn
• Himan Abdollahpouri on X
• Himan's Website
• Himan's PhD Thesis on "Popularity Bias in Recommendation: A Multi-stakeholder Perspective"
• 2nd Workshop on Multi-Objective Recommender Systems (MORS @ RecSys 2022)

Papers:

• Su et al. (2009): A Survey on Collaborative Filtering Techniques
• Mehrotra et al. (2018): Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommender Systems
• Abdollahpouri et al. (2021): User-centered Evaluation of Popularity Bias in Recommender Systems
• Abdollahpouri et al. (2019): The Unfairness of Popularity Bias in Recommendation
• Abdollahpouri et al. (2017): Controlling Popularity Bias in Learning-to-Rank Recommendation
• Wasilewsi et al. (2016): Incorporating Diversity in a Learning to Rank Recommender System
• Oh et al. (2011): Novel Recommendation Based on Personal Popularity Tendency
• Steck (2018): Calibrated Recommendations
• Abdollahpouri et al. (2023): Calibrated Recommendations as a Minimum-Cost Flow Problem
• Seymen et al. (2022): Making smart recommendations for perishable and stockout products

General Links:

• Follow me on LinkedIn
• Follow me on X
• Send me your comments, questions and suggestions to marcel@recsperts.com
• Recsperts Website