# Wide & Deep Learning for Recommender Systems

**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  
**arXiv:** [1606.07792](https://arxiv.org/abs/1606.07792)  
**Date:** 2016-06-24  
**Summary page:** https://www.summarizepaper.com/en/paper/1606.07792v1/

## Key points

- Generalized linear models with nonlinear feature transformations are discussed for large-scale regression and classification problems with sparse inputs.
- These models can memorize feature interactions through cross-product feature transformations but require more feature engineering effort for generalization.
- Deep neural networks can generalize better to unseen feature combinations by learning low-dimensional dense embeddings for sparse features.
- However, deep neural networks with embeddings can over-generalize and recommend less relevant items when user-item interactions are sparse and high-rank.
- The proposed method called Wide & Deep learning combines wide linear models and deep neural networks to address these challenges.
- Wide linear models focus on memorizing feature interactions while deep neural networks capture generalization through learned embeddings.
- The system was evaluated on Google Play, a commercial mobile app store with over one billion active users and one million apps.
- Online experiment results showed that Wide & Deep significantly increased app acquisitions compared to models using only wide or deep components.
- The authors have open-sourced their implementation in TensorFlow.
- Overall, this approach combines the benefits of memorization and generalization for recommender systems using both wide linear models and deep neural networks.

## Summary

The paper discusses the use of generalized linear models with nonlinear feature transformations for large-scale regression and classification problems with sparse inputs. These models are effective in memorizing feature interactions through cross-product feature transformations, but generalization requires more feature engineering effort. On the other hand, deep neural networks can generalize better to unseen feature combinations by learning low-dimensional dense embeddings for sparse features. However, when user-item interactions are sparse and high-rank, deep neural networks with embeddings can over-generalize and recommend less relevant items. To address these challenges, the authors propose a method called Wide & Deep learning which combines wide linear models and deep neural networks. The wide linear models focus on memorization of feature interactions while the deep neural networks capture generalization through learned embeddings. The system was evaluated on Google Play, a commercial mobile app store with over one billion active users and one million apps. Online experiment results showed that Wide & Deep significantly increased app acquisitions compared to models using only wide or deep components. The authors have also open-sourced their implementation in TensorFlow. Overall, this paper presents an approach that combines the benefits of memorization and generalization for recommender systems using both wide linear models and deep neural networks. The experimental results demonstrate its effectiveness in improving app acquisitions in a real-world setting.