[1606.07792] Wide & Deep Learning for Recommender Systems

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active u

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@_arohan_ @srchvrs @hardmaru Yeah story as old as time itself, Linear + Deep t.co/45J6bDIPbF Transformer Encoder + LSTM decoder t.co/qTn53sBwXl Convolutions + ViT t.co/o7FzQY35od Transformer with convs t.co/Xh0IazlNhp

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