184 citations · 190 across the 8 of their papers we have counts for
3 papers · 1 filter
Neural Click Models for Recommender Systems
Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev +4
We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models.…
ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering
Ilya Shenbin, Sergey Nikolenko
We present ImplicitSLIM, a novel unsupervised learning approach for sparse high-dimensional data, with applications to collaborative filtering. Sparse linear methods (SLIM) and the…
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Ilya Shenbin, Anton Alekseev, Elena Tutubalina +2
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering. In particular, the recently proposed Mult-VAE model, which…