9 citations · 17 across the 6 of their papers we have counts for
6 papers
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…
Wide Network Learning with Differential Privacy
Huanyu Zhang, Ilya Mironov, Meisam Hejazinia
Despite intense interest and considerable effort, the current generation of neural networks suffers a significant loss of accuracy under most practically relevant privacy training…
Accelerated learning from recommender systems using multi-armed bandit
Meisam Hejazinia, Kyler Eastman, Shuqin Ye +2
Recommendation systems are a vital component of many online marketplaces, where there are often millions of items to potentially present to users who have a wide variety of wants o…
Deep Personalized Re-targeting
Meisam Hejazinia, Pavlos Mitsoulis-Ntompos, Serena Zhang
Predicting booking probability and value at the traveler level plays a central role in computational advertising for massive two-sided vacation rental marketplaces. These marketpla…
A/B Testing Measurement Framework for Recommendation Models Based on Expected Revenue
Meisam Hejazinia, Majid Hosseini, Bryant Sih
We provide a method to determine whether a new recommendation system improves the revenue per visit (RPV) compared to the status quo. We achieve our goal by splitting RPV into conv…
A Simple Deep Personalized Recommendation System
Pavlos Mitsoulis-Ntompos, Meisam Hejazinia, Serena Zhang +1
Recommender systems are critical tools to match listings and travelers in two-sided vacation rental marketplaces. Such systems require high capacity to extract user preferences for…