92 citations · 101 across the 4 of their papers we have counts for
4 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…
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…
Recommender Systems for the Conference Paper Assignment Problem
Don Conry, Yehuda Koren, Naren Ramakrishnan
Conference paper assignment, i.e., the task of assigning paper submissions to reviewers, presents multi-faceted issues for recommender systems research. Besides the traditional goa…