23 citations · 65 across the 19 of their papers we have counts for
4 papers · 1 filter
Private Matrix Factorization with Public Item Features
Mihaela Curmei, Walid Krichene, Li Zhang +1
We consider the problem of training private recommendation models with access to public item features. Training with Differential Privacy (DP) offers strong privacy guarantees, at…
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…
Neural Collaborative Filtering vs. Matrix Factorization Revisited
Steffen Rendle, Walid Krichene, Li Zhang +1
Embedding based models have been the state of the art in collaborative filtering for over a decade. Traditionally, the dot product or higher order equivalents have been used to com…
Scalable Realistic Recommendation Datasets through Fractal Expansions
Francois Belletti, Karthik Lakshmanan, Walid Krichene +2
Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap w…