4 citations · 4 across the 3 of their papers we have counts for
3 papers
Efficient user history modeling with amortized inference for deep learning recommendation models
Lars Hertel, Neil Daftary, Fedor Borisyuk +2
We study user history modeling via Transformer encoders in deep learning recommendation models (DLRM). Such architectures can significantly improve recommendation quality, but usua…
Neural Optimization with Adaptive Heuristics for Intelligent Marketing System
Changshuai Wei, Benjamin Zelditch, Joyce Chen +6
Computational marketing has become increasingly important in today's digital world, facing challenges such as massive heterogeneous data, multi-channel customer journeys, and limit…
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…