Talk given by Florian Strub during the RecsysFR meetup on June 22nd 2016.

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Inria, Sequel – Meetup Juin 2016
Deep Learning
Collaborative
Filtering
and

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Collaborative filtering
Bob

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Bob
Collaborative filtering
Tim

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Bob
Tim
Collaborative filtering

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Collaborative filtering
Bob
Tim

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Collaborative filtering
Bob
Tim

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Collaborative filtering
Bob
Tim

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Goal is to predict the rating Tim
would have given to Jurassic Park
Collaborative filtering
Tim

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Collaborative filtering

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Goal : Predict the missing rating
Collaborative filtering
Bob ? 3 5 ?
Ana ? 4 ? 5
Elain 5 ? 4 ?
Sulman 2 ? 1 4

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Goal : Predict the missing rating
Bob 4 3 5 1
Ana 2 4 2 5
Elain 5 4 4 3
Sulman 2 3 1 4
Collaborative filtering

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Goal : Predict the missing rating
Bob 4 3 5 1
Ana 2 4 2 5
Elain 5 4 4 3
Sulman 2 3 1 4
To be recommended
Collaborative filtering

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Bob ? 3 5 ?
Ana ? 4 ? 5
Elain 5 ? 4 ?
Sulman 2 ? 1 4
Score: Average|Predicted rating – real rating|
Collaborative filtering

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Bob ? 3 5 ?
Ana ? 4 ? 5
Elain 5 ? 4 ?
Sulman 2 ? 1 4
Score = Average |Predicted rating – real rating|2
Collaborative filtering

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Neural Networks

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Input
Neural Networks

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Forward
Backward
The model is updated to fit the
target
The error is backpropagated into
the model
Neural Networks

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Neural Networks

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RUsers
Items v
u
Neural Networks

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RUsers
Items v
u
Neural Networks

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RUsers
Items v
u
Neural Networks

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? 3 4 ? 1
4.9 2.8 4 .1 2.0 1.1
𝑅
user
𝑅
i
t
e
m
U-CFN V-CFN
Neural Networks

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? 3 4 ? 1
4.9 2.8 4 .1 2.0 1.1
inputs
Dimension
reduction
Input reconstruction
5 3 4 2 1
4.9 2.8 4 .1 2.0 1.1
The model

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? 3 4 ? 1
4.9 2.8 4 .1 2.0 1.1
3 4 1
2.8 4 .1 1.1
Training stepTesting step
The model

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1 ? 2 ? 1
1.2 4.9 1.1
1 0 2 0 1
1.2 2.1 4.9 2.0 1.1
Forward
The model

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1 ? 2 ? 1
1.2 4.9 1.1
1 0 2 0 1
0.2 0 -0.1 0 0.1
Backward
The model

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β*0.2 0 α*1.1 0 β*1.1
1 0 2 0 1
1 0 0 0 1
Forward
1.2 2.3 3.1 4.2 1.1
Backward
1 ? 2 ? 1
True input
0.2 0 1.1 0 0.1
error 0.2 ? 1.1 ? 0.1
α = Prediction = supervized
β = Reconstruction = unsupervized
The model
1 ? 2 ? 1

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β*0.2 0 α*1.1 0 β*1.1
1 0 2 0 1
1 0 0 0 1
Forward
1.2 2.3 3.1 4.2 1.1
Backward
1 ? 2 ? 1
True input
0.2 0 1.1 0 0.1
error 0.2 ? 1.1 ? 0.1
α = Prediction = supervized
β = Reconstruction = unsupervized
The model
1 ? 2 ? 1

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The model
300.000 to 50.000 entries
Real
life

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MovieLens :
- 10 million ratings : 72.000 users / 10.000 movies
- 20 million ratings : 138.000 users / 28.000 movies
Results

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Quadratic error
MovieLens-10M MovieLens-20M
BPMF 0.8213 0.8123
ALS-WR 0.7830 0.7746
LLORMA 0.7949 0.7843
U-CFN 0.7767 0.7663
V-CFN 0.7767 0.7663
RMSE for train/test 90/10
BPMF : rank=10
ALS-WE : rank = 200
LLORMA : rank = 20, anchor point = 30
A. Mnih and R. Salakhutdinov, “Probabilistic matrix factorization,” in Advances in neural information processing systems, 2007, pp. 1257– 1264.
B. J. Lee, S. Kim, G. Lebanon, and Y. Singerm, “Local low-rank matrix approximation,” in Proc. of ICML’13, 2013, pp. 82–90.
C. Y.Zhou,D.Wilkinson,R.Schreiber,andR.Pan,“Large-scaleparallel collaborative filtering for the netflix prize,” in Algorithmic Aspects in Information and Management. Springer, 2008, pp. 337–348.
Results

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Singular value decomposition (SVD)
Other algorithms:
●Alternating Least Square Weighted Lambda-Regularization (ALS-WR)
●Probabilistic Matrix Factorization (PMF, BPMF, NLPMF)
●Local Low Rank Matrix Approximation (LLORMA)
RUsers
Items v
u
Link with matrix factorization (optional)

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RUsers
Items v
u
z = W3 𝐚𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧2
ri = Vui
activation
Link with matrix factorization (optional)

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ui
V
Link with matrix factorization (optional)
activation
ri = Vui
RUsers
Items v
u
CFN computes a Non-Linear
Matrix Factorization

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Discussion
Why nobody tried it? Image/sounds
are dense
Few works
about sparsity
No tools!

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Discussion
Torch framework
Reproduce results

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? 3 4 ? 1
4.9 2.8 4 .1 2.0 1.1
Actor :
Dicaprio
Genre:
action
Year:
2010
Adding external information

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4.9 2.8 4 .1 2.0 1.1
Content-based Filtering
Extensions

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Thank you for listening!

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Input reconstruction
5 ? 4 ? 1
4.9 2.8 4 .1 2.0 1.1
Encoder
Decoder
Autoencoders:
Matrix factorization
z = ℎ Wui,𝐝𝐞𝐧𝐬𝐞
ui,𝐬𝐩𝐚𝐫𝐬𝐞
ui,𝐝𝐞𝐧𝐬𝐞
Link with Matrix Factorization