3 papers
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
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…
cs.IR2025
Item-Language Model for Conversational Recommendation
Li Yang, Anushya Subbiah, Hardik Patel +5
Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abili…