2 citations · 5 across the 7 of their papers we have counts for
7 papers
A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second
Blaž Škrlj, Benjamin Ben-Shalom, Grega Gašperšič +5
Field-aware Factorization Machines (FFMs) have emerged as a powerful model for click-through rate prediction, particularly excelling in capturing complex feature interactions. In t…
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems
Blaž Škrlj, Nir Ki-Tov, Lee Edelist +5
Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams. We introduce Drifter, an efficient and lightweight system for online feat…
Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models
Jan Hartman, Assaf Klein, Davorin Kopič +1
In this work, we introduce the notion of Context-Based Prediction Models. A Context-Based Prediction Model determines the probability of a user's action (such as a click or a conve…
Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations
Blaž Škrlj, Adi Schwartz, Jure Ferlež +2
Hyperparameter optimization is the process of identifying the appropriate hyperparameter configuration of a given machine learning model with regard to a given learning task. For s…
Feature embedding in click-through rate prediction
Samo Pahor, Davorin Kopič, Jure Demšar
We tackle the challenge of feature embedding for the purposes of improving the click-through rate prediction process. We select three models: logistic regression, factorization mac…
Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding
Jan Hartman, Davorin Kopič
In this work, we present a scalable and efficient system for exploring the supply landscape in real-time bidding. The system directs exploration based on the predictive uncertainty…
Scaling TensorFlow to 300 million predictions per second
Jan Hartman, Davorin Kopič
We present the process of transitioning machine learning models to the TensorFlow framework at a large scale in an online advertising ecosystem. In this talk we address the key cha…