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About Wide and Deep Learning for Recommender Systems - Google Play Store - Highlighted Paper
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 featuretransformations are effective and interpretable, while gener-alization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize bet-ter to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalizeand recommend less relevant items when the user-item inter-actions are sparse and high-rank. In this paper, we presentWide & Deep learning—jointly trained wide linear modelsand deep neural networks—to combine the benefits of mem-orization and generalization for recommender systems. We productionized and evaluated the system on Google Play,a commercial mobile app store with over one billion activeusers and over one million apps. Online experiment resultsshow that Wide & Deep significantly increased app acquisi-tions compared with wide-only and deep-only models. Wehave also open-sourced our implementation in TensorFlow.
Detailed Information
| Author: | ['Shubham Chaudhary'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 4 |
| Language: | English |
| Format: | |
| Price: | FREE |
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