Day 3 of #Recsys2022: our favorite 5 papers and talks

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2022-09-22 16:00:11

The last day at RecSys 2022 started with a session on Sessions and Interaction, moved on to Models and Learning to finish with Large-Scale Recommendations. Here are our favorite 5 papers and talks of the day.

Recommendation models like Wide & Deep, DeepFM have always had an inelegant feel to them as they’re essentially two architectures concatenated together to solve the problems of each other. This work proposes a more elegant formulation that combines these components into a unified architecture.

They introduce FiFa: Fieldwise factorized neural networks. The architecture can represent both modern factorization machines (FM) and ReLU neural networks (DNN) in a general form. Recovering FMs or DNNs then becomes a matter of modifying the activation functions. They then show that an activation function exists which can adaptively learn to select the optimal paradigm for each use case.

Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research. It 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, such as autoencoders and other item-based collaborative filtering methods. The authors revisit the well-studied benchmarks where iALS was reported to perform poorly and show that with proper tuning its performance is comparable with state-of-the-art methods.

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