9 citations · 10 across the 4 of their papers we have counts for
5 papers
TransX Technical Report: Scaling Sequence Transduction for Recommendation
Da Xu, Liyan Fang, Divya Venugopalan +5
We present TransX, a sequence-transduction framework and co-designed infrastructure for scaling industrial recommendation with explicit control over model-compute allocation and ex…
TransX: Scaling Transformer-based Recommendation via Behavioral and Serving Stream Crossings
Da Xu, Liyan Fang, Divya Venugopalan +5
Modern industrial recommender systems (RecSys) increasingly adopt Transformer-based sequence models, with an emerging paradigm that frames recommendation as next-token prediction o…
LiMAML: Personalization of Deep Recommender Models via Meta Learning
Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava +10
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases cont…
A/B Testing for Recommender Systems in a Two-sided Marketplace
Preetam Nandy, Divya Venugopalan, Chun Lo +1
Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on…
Achieving Fairness via Post-Processing in Web-Scale Recommender Systems
Preetam Nandy, Cyrus Diciccio, Divya Venugopalan +3
Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society. We extended the definitions of two commonly accepted notions of f…