#19: Popularity Bias in Recommender Systems with Himan Abdollahpouri
Oct 12, 2023
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Himan Abdollahpouri, Applied Research Scientist at Spotify, delves into popularity bias in recommender systems. Topics include unfair recommendations for stakeholders, challenges in music and podcast streaming personalization, and strategies to counteract popularity bias. Learn about debiasing data, models, and outputs, as well as the relationship between multi-objective and multi-stakeholder recommender systems.
Ensure a fair distribution of popular, kind of popular, and non-popular items in recommendations for diverse user preferences.
Tailor recommendations to individual preferences for popular versus niche content to improve personalization.
Mitigate popularity bias in real-world applications like music streaming platforms to offer fair exposure to various artists and ensure a diverse recommendation experience.
Deep dives
Understanding Popularity Bias in Recommender Systems
Popularity bias in recommendation focuses on overly promoting popular items over others, leading to an imbalance in recommendations. It is crucial to ensure a fair distribution of very popular, kind of popular, and non-popular items in recommendations to cater to diverse user preferences.
Incorporating User Tendencies for Personalization
Recognizing that users differ in their preferences for popular content, personalized recommendations should align with individual tendencies. By understanding users' inclination towards popular versus niche items, algorithms can tailor recommendations to match their specific interests, improving personalization.
Mitigating Popularity Bias in Real-World Applications
Implementing strategies to mitigate popularity bias is essential in real-world applications like music streaming platforms. By calibrating recommendations to reduce overreliance on popular content, platforms can offer fair exposure to various artists, ensuring a balanced and diverse recommendation experience for users.
Ensuring Fairness and Diversity in Recommendations
Addressing popularity bias not only enhances recommendation accuracy but also promotes fairness and diversity. By giving equal opportunities to new and lesser-known artists, recommender systems can foster a more inclusive platform that supports emerging creators and provides a diverse range of content to users.
Overview of Multi-Objective Recommender Systems
Multi-objective recommender systems aim to consider multiple criteria and objectives when generating recommendations for users. While relevance to the user is the primary objective, other important objectives might include supporting less known creators, recommending products that may go out of stock, or catering to specific health needs. The challenge lies in incorporating all these objectives into the recommendation process to ensure a holistic approach that benefits the user.
Addressing Fairness and Diverse Goals in Recommender Systems
Balancing user interactions and explicit preferences plays a crucial role in improving recommendation systems. The challenge remains in interpreting user signals correctly to provide relevant and satisfying recommendations over the long term. Calibration algorithms, such as those focusing on genre preferences, offer solutions to tailor recommendations effectively. Successful personalization aims to address diverse user needs and ensure a balanced approach to serving multiple stakeholders for optimal user satisfaction.
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. Don't forget to follow the podcast and please leave a review
(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
