394 citations · 1.4k across the 41 of their papers we have counts for
6 papers · 1 filter
Understanding Scaling Laws for Recommendation Models
Newsha Ardalani, Carole-Jean Wu, Zeliang Chen +2
Scale has been a major driving force in improving machine learning performance, and understanding scaling laws is essential for strategic planning for a sustainable model quality p…
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis +7
Federated learning (FL) has emerged as an effective approach to address consumer privacy needs. FL has been successfully applied to certain machine learning tasks, such as training…
Infinite Recommendation Networks: A Data-Centric Approach
Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu +1
We leverage the Neural Tangent Kernel and its equivalence to training infinitely-wide neural networks to devise -AE: an autoencoder with infinitely-wide bottleneck layers.…
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Kiwan Maeng, Haiyu Lu, Luca Melis +3
Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device. While prior FL optimizations tackled…
SVP-CF: Selection via Proxy for Collaborative Filtering Data
Noveen Sachdeva, Carole-Jean Wu, Julian McAuley
We study the practical consequences of dataset sampling strategies on the performance of recommendation algorithms. Recommender systems are generally trained and evaluated on sampl…
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi +21
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks…