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Neural Collaborative Filtering vs. Matrix Factorization Revisited
[article]
2020
arXiv
pre-print
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 combine two or more embeddings, e.g., most notably in matrix factorization. In recent years, it was suggested to replace the dot product with a learned similarity e.g. using a multilayer perceptron (MLP). This approach is often referred to as neural collaborative filtering (NCF). In this work, we revisit the experiments
arXiv:2005.09683v2
fatcat:mkzgijamdndnxo4wnfd5fmo7cq