· 2016
Wide & Deep Learning for Recommender Systems
- 3,369citations
- 2016year
Short summary
Wide & Deep learning, a hybrid model combining wide linear models and deep neural networks, significantly increased app acquisitions on Google Play by jointly leveraging memorization and generalization.
AI-generated from the title and abstract; the full text is not read.
Key points
- Wide & Deep learning jointly trains wide linear models and deep neural networks.
- Wide models excel at memorization through cross-product feature transformations.
- Deep models provide generalization by learning low-dimensional dense embeddings.
- The hybrid approach was productionized and tested on Google Play, a large-scale commercial app store.
- Online experiments showed significant increases in app acquisitions compared to wide-only and deep-only models.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Information Systems
Information SystemsComputer Science