Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems

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In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques and their latent factor models to neural approaches. However, given the proven power of latent factor models, some newer neural approaches incorporate them within more complex network architectures. One specific idea, re...More