3 citations · 3 across the 3 of their papers we have counts for
3 papers
Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders
Zhimin Chen, Chenyu Zhao, Ka Chun Mo +7
Modern large-scale recommendation systems rely heavily on user interaction history sequences to enhance the model performance. The advent of large language models and sequential mo…
CAViaR: Context Aware Video Recommendations
Khushhall Chandra Mahajan, Aditya Palnitkar, Ameya Raul +1
Many recommendation systems rely on point-wise models, which score items individually. However, point-wise models generating scores for a video are unable to account for other vide…
PIE: Personalized Interest Exploration for Large-Scale Recommender Systems
Khushhall Chandra Mahajan, Amey Porobo Dharwadker, Romil Shah +3
Recommender systems are increasingly successful in recommending personalized content to users. However, these systems often capitalize on popular content. There is also a continuou…