Wide & Deep Learning for Recommender Systems Wide & Deep Learning for Recommender Systems
Paper summary TLDR; The authors jointly train a Logistic Regression Model with sparse features that is good at "memorization" and a deep feedforward net with embedded sparse features that is good at "generalization". The model is live in the Google Play store and has achieved a 3.9% gain in app acquisiton as measured by A/B testing. #### Key Points - Wide Model (Logistic Regression) gets cross product of binary features, e.g. "AND(user_installed_app=netflix, impression_app=pandora") as inputs. Good at memorization. - Deep Model alone has a hard time to learning embedding for cross-product features because no data for most combinations but still makes predictions. - Trained jointly on 500B examples.
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Wide & Deep Learning for Recommender Systems
Heng-Tze Cheng and Levent Koc and Jeremiah Harmsen and Tal Shaked and Tushar Chandra and Hrishi Aradhye and Glen Anderson and Greg Corrado and Wei Chai and Mustafa Ispir and Rohan Anil and Zakaria Haque and Lichan Hong and Vihan Jain and Xiaobing Liu and Hemal Shah
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.LG, cs.IR, stat.ML

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