Beyond the Buzz: Decoding Popularity Bias in Large-Scale Recommender Systems


Large-scale recommender systems play a pivotal role in personalized content discovery, assisting users to find relevant items. However, the algorithms powering these systems often grapple with popularity bias, gravitating towards recommending widely popular items while sidelining lesser-known gems. This poses problems for both consumers and content creators and also fosters undesired reinforcement effects over time. In this talk, we'll delve into the intricate landscape of popularity bias in billion-scale recommender systems and review existing approaches to detect, quantify and mitigate it. We will also identify some of the open challenges in this area and offer a glimpse into the exciting future directions that are currently being explored. Through a nuanced and thorough examination of popularity bias in online recommendation systems, we hope to help you better navigate this tricky space and emerge with a deeper understanding of the complexities and opportunities at play. Let's uncover the mystery of popularity bias and strive to cultivate fairness and inclusivity in content recommendations!


Amey Porobo Dharwadker is a seasoned Machine Learning Engineering Manager at Meta, leading the Facebook Video Recommendations Ranking team. He is renowned for his pivotal role in developing personalization models used by billions of global users everyday, which have significantly contributed to Facebook's impressive user growth. His contributions have led to the success of Facebook Watch and Reels, now engaging over 1.25 billion monthly users. Prior to this, he made substantial strides in improving user engagement and revenue growth at Facebook through his work on News Feed and Ads Machine Learning. He is a prolific researcher with multiple international publications in recommender systems, and he actively serves as a program committee member for top-tier AI conferences including AAAI, AISTATS, IJCAI, CIKM and ECIR. As a thought leader, Amey is a sought-after speaker at prestigious AI venues and contributes to hackathons, angel syndicates and startup accelerators as a mentor. He also plays a significant role on the juries of renowned global technology competitions, including the CES Innovation Awards and Edison Awards. He holds a Master's degree from Columbia University in the City of New York and a Bachelor's degree from the National Institute of Technology Tiruchirappalli, India.

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