3 papers
cs.AI2026
Agentic ML Exploration (A-MLE) for Ads Ranking
Erwin Gao, Vinodh Kumar Sunkara, Jingyi Guan +36
Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iteration - the cycles of research,…
cs.IR2025
Balancing Semantic Relevance and Engagement in Related Video Recommendations
Amit Jaspal, Feng Zhang, Wei Chang +5
Related video recommendations commonly use collaborative filtering (CF) driven by co-engagement signals, often resulting in recommendations lacking semantic coherence and exhibitin…
cs.IR2025
Finding Interest Needle in Popularity Haystack: Improving Retrieval by Modeling Item Exposure
Rahul Agarwal, Amit Jaspal, Saurabh Gupta +1
Recommender systems operate in closed feedback loops, where user interactions reinforce popularity bias, leading to over-recommendation of already popular items while under-exposin…