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Recurrent Recommender NetworksChao-Yuan Wu1, Amr Ahmed2, Alex Beutel2
Alex Smola3, How Jing4
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Traditional recommender systems
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3 5 1
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Predict missing values
Observe interactions
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Traditional recommender systemsassume stationary states
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f(vu, vm)u
mUser states
Movie states3
However…, user & movie states should be time-dependent.
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User preference changes over time
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?10 years ago
now
Movie reception changes over time
So bad that it’s great to watch
Bad movie6
Exogenous effects“La La Land” won big at Golden Globes
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Seasonal effects
Only watch during Christmas
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Traditional MF
Modeling temporal dynamics within each user and movieTraditional MF
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Also…, we consider real prediction instead of interpolation.
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Kid started using
Consider a user profile….
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Traditional random split
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Real scenario
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Real scenario
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In this paper we design and evaluate our model with this real scenario.
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Recurrent Recommender Networks
User RNN
Movie RNN
user
movie16
User Recurrent Neural Network (User RNN)
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User Recurrent Neural Network (User RNN)
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Rating
Movie RNN
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Rating
Recurrent Recommender Networksuser
movie 20
Experiments
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Rating prediction accuracy
PMF: Salakhutdinov & Mnih NIPS ‘07T-STD: Koren KDD ‘09U-AR & I-AR: Sedhain et al. WWW ‘15
(RMSE)
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How does the model react to the temporal effects?
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Automatically captures exogenous effects
Oscar & Golden globe
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Automatically captures system-wise effects
Netflix changed the Likert scale
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Automatically models effects that used to be captured by hand-crafted features
Movie age effects
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Large improvement when movies have large fluctuations
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Improvement
Fluctuation
SummaryNovel model
SummaryNovel model Future prediction
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SummaryNovel model Future prediction
Accurate prediction
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SummaryNovel model Future prediction
Accurate prediction Temporal dynamics
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