Wide & Deep Learning for Recommender Systems
Venue
arXiv:1606.07792 (2016)
Publication Year
2016
Authors
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, Hemal Shah
BibTeX
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 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.
