• Corpus ID: 239998170

Revisiting the Performance of iALS on Item Recommendation Benchmarks

  title={Revisiting the Performance of iALS on Item Recommendation Benchmarks},
  author={Steffen Rendle and Walid Krichene and Li Zhang and Yehuda Koren},
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 computationally efficient and scalable collaborative filtering methods. However, recent studies suggest that its prediction quality is not competitive with the current state of the art, in particular autoencoders and other item-based collaborative filtering methods. In this work, we revisit the iALS algorithm and present… 
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