92 citations · 102 across the 5 of their papers we have counts for
6 papers
iALS++: Speeding up Matrix Factorization with Subspace Optimization
Steffen Rendle, Walid Krichene, Li Zhang +1
iALS is a popular algorithm for learning matrix factorization models from implicit feedback with alternating least squares. This algorithm was invented over a decade ago but still…
Revisiting the Performance of iALS on Item Recommendation Benchmarks
Steffen Rendle, Walid Krichene, Li Zhang +1
Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications. iALS is known to be one of the most com…
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Steve Chien, Prateek Jain, Walid Krichene +4
We study the problem of differentially private (DP) matrix completion under user-level privacy. We design a joint differentially private variant of the popular Alternating-Least-Sq…
Superbloom: Bloom filter meets Transformer
John Anderson, Qingqing Huang, Walid Krichene +2
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids. This is achieved by applying hash functions to map each id to multiple hash to…
On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
Steffen Rendle, Li Zhang, Yehuda Koren
Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems. In this paper, we show that running baselines properly is diff…
Efficient Training on Very Large Corpora via Gramian Estimation
Walid Krichene, Nicolas Mayoraz, Steffen Rendle +5
We study the problem of learning similarity functions over very large corpora using neural network embedding models. These models are typically trained using SGD with sampling of r…