5 papers
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…