Wide & Deep Learning for Recommender Systems

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Authors Vihan Jain, Levent Koc, Heng-Tze Cheng, Tal Shaked, Zakaria Haque, Hrishi Aradhye, Jeremiah Harmsen, Rohan Anil, Lichan Hong, Xiaobing Liu, Glen Anderson, Wei Chai, Mustafa Ispir, Tushar Chandra, Hemal Shah, Greg Corrado
Journal/Conference Name ACM International Conference Proceeding Series
Paper Category
Paper Abstract 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 users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.
Date of publication 2016
Code Programming Language Multiple

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