2016 WideDeepLearningforRecommenderS

• (Cheng et al., 2016) ⇒ 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, and Hemal Shah. (2016). “Wide & Deep Learning for Recommender Systems.” In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ISBN:978-1-4503-4795-2 doi:10.1145/2988450.2988454

Subject Headings: Deep Recommender Algorithm, Wide and Deep Recommender Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+Wide+%26+Deep+Learning+for+Recommender+Systems
• http://dl.acm.org/citation.cfm?id=2988450.2988454&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 WideDeepLearningforRecommenderS	Tal Shaked Tushar Chandra Greg Corrado Jeremiah Harmsen Heng-Tze Cheng Levent Koc Hrishi Aradhye Glen Anderson Wei Chai Mustafa Ispir Rohan Anil Zakaria Haque Lichan Hong Vihan Jain Xiaobing Liu Hemal Shah			Wide & Deep Learning for Recommender Systems				10.1145/2988450.2988454		2016