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cs.IR2026
Breaking the Loop: An Empirical Comparison of Strategies for Novelty and Freshness in YouTube Music
Srivaths Ranganathan, Zihuan Diao, Bernardo Cunha +7
Continuously trained ranking models in music recommenders fall into feedback loops where previously consumed items dominate recommendations. This suppresses two distinct content cl…
cs.IR2026
Multi-Agent Video Recommenders: Evolution, Patterns, and Open Challenges
Srivaths Ranganathan, Abhishek Dharmaratnakar, Anushree Sinha +1
Video recommender systems are among the most popular and impactful applications of AI, shaping content consumption and influencing culture for billions of users. Traditional single…
cs.IR2026
Zero-shot Cross-domain Knowledge Distillation: A Case study on YouTube Music
Srivaths Ranganathan, Nikhil Khani, Shawn Andrews +8
Knowledge Distillation (KD) has been widely used to improve the quality of latency sensitive models serving live traffic. However, applying KD in production recommender systems wit…