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The aim is to combine memorization and generalization benefits in recommender systems using Wide & Deep learning.

September 15, 2016Open Access

Wide & Deep Learning for Recommender Systems

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The aim is to combine memorization and generalization benefits in recommender systems using Wide & Deep learning.
Developed Wide & Deep learning model combining wide linear models and deep neural networks.
Implemented and evaluated the system on Google Play, with over one billion active users.
Conducted online experiments comparing app acquisitions across different model types.
Wide & Deep learning significantly increased app acquisitions compared to wide-only and deep-only models.
Results indicate improved user engagement with the integrated approach.
Performance metrics from the online experiment support the benefits of combined learning.

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Randomized trial reveals enhanced app acquisitions using Wide & Deep learning in a commercial mobile app store, suggesting improved user interaction.

Cheng et al. (2016) studied this question.

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Randomized trial reveals enhanced app acquisitions using Wide & Deep learning in a commercial mobile app store, suggesting improved user interaction.