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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 ? 5Elain 5 ? 4 ?Sulman 2 ? 1 4

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Goal : Predict the missing rating

Bob 4 3 5 1Ana 2 4 2 5Elain 5 4 4 3Sulman 2 3 1 4

Collaborative filtering

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Goal : Predict the missing rating

Bob 4 3 5 1Ana 2 4 2 5Elain 5 4 4 3Sulman 2 3 1 4

To be recommended

Collaborative filtering

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Bob ? 3 5 ?Ana ? 4 ? 5Elain 5 ? 4 ?Sulman 2 ? 1 4

Score: Average|Predicted rating – real rating|

Collaborative filtering

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Bob ? 3 5 ?Ana ? 4 ? 5Elain 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

Plane ?

Dog ?

Cat ?

Boat ?

Truck ?

Neural Networks

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Plane ?

Dog ?

Cat ?

Boat ?

Forw

ard

Back

war

d

The model is updated to fit the target

The error is backpropagated into the model

Truck ?

Neural Networks

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Plane ?

Dog ?

Cat ?

Boat ?

Truck ?

Neural Networks

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5 3 1 4

RUsers

Items v

u

2

Neural Networks

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5 ? ? 4 sparse

RUsers

Items v

u

2

5 3 1 4 2

Neural Networks

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sparse

dense

RUsers

Items v

u

5 ? ? 4 2

5 3 1 4 2

Neural Networks

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? 3 4 ? 1

4.9 2.8 4 .1 2.0 1.1

�̂�user �̂�item

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

Forw

ard

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

Back

war

d

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

Back

war

d

1 ? 2 ? 1True input

0.2 0 1.1 0 0.1error 0.2 ? 1.1 ? 0.1

α = Prediction = supervizedβ = 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

Back

war

d

1 ? 2 ? 1True input

0.2 0 1.1 0 0.1error 0.2 ? 1.1 ? 0.1

α = Prediction = supervizedβ = 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-20MBPMF 0.8213 0.8123ALS-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=10ALS-WE : rank = 200LLORMA : 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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dense

sparse

RUsers

Items v

u

5 ? ? 4 2

5 3 1 4 2

z=W 3𝐚𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧2

ri=V ui

activ

ation

Link with matrix factorization (optional)

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u i

V

Link with matrix factorization (optional)

5 ? ? 4 2

5 3 1 4 2

activ

ation

ri=V uidense

sparse

RUsers

Items v

u

CFN computes a Non-Linear Matrix Factorization

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Why nobody tried it?

Image/sounds are dense

Few works about sparsity

No tools!

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DiscussionTorch 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 factorizationz=h (W ui ,𝐝𝐞𝐧𝐬𝐞 )

u i ,𝐬𝐩𝐚𝐫𝐬𝐞

u i ,𝐝𝐞𝐧𝐬𝐞

Link with Matrix Factorization