MACDAE infers implicit contexts via a constrained autoencoder and integrates them into an end-to-end O2O recommender, reporting gains on Yelp/Dianping/Koubei and 2.9%/5.6% lifts in online CTR/conversion.
Greedy layer-wise training of deep networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
An autoencoder on 10^12-point stratified turbulence data identifies vertical velocity as a key marker for turbulence features via bleed-over in reconstruction errors.
citing papers explorer
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Infer Implicit Contexts in Real-time Online-to-Offline Recommendation
MACDAE infers implicit contexts via a constrained autoencoder and integrates them into an end-to-end O2O recommender, reporting gains on Yelp/Dianping/Koubei and 2.9%/5.6% lifts in online CTR/conversion.
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Unsupervised Machine Learning to Teach Fluid Dynamicists to Think in 15 Dimensions
An autoencoder on 10^12-point stratified turbulence data identifies vertical velocity as a key marker for turbulence features via bleed-over in reconstruction errors.