View
5
Download
0
Category
Preview:
Citation preview
Recurrent Recommender NetworksChao-Yuan Wu1, Amr Ahmed2, Alex Beutel2
Alex Smola3, How Jing4
21 3 4
1
Traditional recommender systems
4
3 5 1
3
2
1 ?
2 5
Predict missing values
Observe interactions
2
Traditional recommender systemsassume stationary states
4
3 5 1
3
2
1 ?
2 5
f(vu, vm)u
mUser states
Movie states3
However…, user & movie states should be time-dependent.
4
User preference changes over time
5
?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
7
Seasonal effects
Only watch during Christmas
8
Traditional MF
Modeling temporal dynamics within each user and movieTraditional MF
10
Also…, we consider real prediction instead of interpolation.
11
Kid started using
Consider a user profile….
12
?
Traditional random split
??
13
Real scenario
?? ??
14
Real scenario
?? ?
In this paper we design and evaluate our model with this real scenario.
?
15
Recurrent Recommender Networks
User RNN
Movie RNN
user
movie16
User Recurrent Neural Network (User RNN)
17
User Recurrent Neural Network (User RNN)
18
Rating
Movie RNN
19
Rating
Recurrent Recommender Networksuser
movie 20
Experiments
21
Rating prediction accuracy
PMF: Salakhutdinov & Mnih NIPS ‘07T-STD: Koren KDD ‘09U-AR & I-AR: Sedhain et al. WWW ‘15
(RMSE)
22
How does the model react to the temporal effects?
23
24
?
Automatically captures exogenous effects
Oscar & Golden globe
25
Automatically captures system-wise effects
Netflix changed the Likert scale
26
Automatically models effects that used to be captured by hand-crafted features
Movie age effects
27
Large improvement when movies have large fluctuations
28
Improvement
Fluctuation
SummaryNovel model
SummaryNovel model Future prediction
? ? ??
SummaryNovel model Future prediction
Accurate prediction
? ? ??
32
SummaryNovel model Future prediction
Accurate prediction Temporal dynamics
? ? ??
Recommended