Deep Context-Aware Recommender System Utilizing Sequential Latent Context

no code implementations • 9 Sep 2019 • Amit Livne, Moshe Unger, Bracha Shapira, Lior Rokach

Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges.

Collaborative Filtering Recommendation Systems

Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate

no code implementations • 26 Jul 2020 • Amit Livne, Roy Dor, Eyal Mazuz, Tamar Didi, Bracha Shapira, Lior Rokach

Learning sophisticated models to understand and predict user behavior is essential for maximizing the CTR in recommendation systems.

Click-Through Rate Prediction Feature Engineering +1

Evolving Context-Aware Recommender Systems With Users in Mind

no code implementations • 30 Jul 2020 • Amit Livne, Eliad Shem Tov, Adir Solomon, Achiya Elyasaf, Bracha Shapira, Lior Rokach

An empirical analysis of our results validates that our proposed approach outperforms SOTA CARS models while improving transparency and explainability to the user.

feature selection Recommendation Systems