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Deep learning:
the future of recommendations
Balázs Hidasi
Head of Data Mining and Research
Gravity meetup @ Startup Safary
April 21, 2016
Deep learning in the headlines
Deep learning in the background
• Life improving services
 Speech recognition
 Personal assistants (e.g. Siri,
Cortana)
 Computer vision, object
recognition
 Machine translation
 Chatbot technology
 Natural Language Processing
 Face recognition
 Self driving cars
• For fun
 Text generation
 Composing music
 Painting pictures
 Etc.
What is deep learning?
• A class of machine learning algorithms
 that use a cascade of multiple non-linear processing layers
 and complex model structures
 to learn different representations of the data in each layer
 where higher level features are derived from lower level
features
 to form a hierarchical representation.
Deep learning is not a new topic
• First deep network proposed in the 1970s
• More papers in the 80s and 90s
• Why now?
 Older research was not used widely in practice
 Applications were much more simplistic that today’s
Neural networks: a brief overview
Neurons, neural networks
• Neuron: rough abstraction of the human neuron
 Receives inputs (signals)
 Sum weighted inputs is big enough  signal
 Amplifiers and inhibitors
 Basic pattern recognition
• Neural network: neurons connected to one another
• Feedforward networks: neurons are organized into
layers
 Connections only between subsequent layers
𝑦
𝑥1
𝑥2
𝑥3
𝑥4
𝑓(. )
𝑖=1
𝑁
𝑤𝑖 𝑥𝑖 + 𝑏
𝑥1
𝑥2
𝑥3
ℎ1
1
ℎ2
1
ℎ3
1
ℎ1
2
ℎ2
2
Networks that big enough: go deep not wide
• Feedforward neural networks are universal
approximators
 Can imitate any function if they are big enough
 (Also needs enough in-out pairs to learn)
• What is big enough?
 Number of layers / neurons
 Theoretical „big enough” conditions massively overshoot
• Go deep, not wide
 The number of neurons required for good approximation is
polynomial in the input if the network is deep enough
 Otherwise it is exponential
Training neural networks
• Forward pass: get the current estimate of the target
o 𝑠𝑗
1
= 𝑖 𝑤𝑖,𝑗
1
𝑥𝑖 + 𝑏𝑗
1
; ℎ𝑗
1
= 𝑓 𝑠𝑗
1
o 𝑠 𝑘
2
= 𝑗 𝑤𝑗,𝑘
2
ℎ𝑗
1
+ 𝑏 𝑘
2
; ℎ 𝑘
2
= 𝑓 𝑠 𝑘
2
o …
o 𝑠𝑙
𝑂
= 𝑘 𝑤 𝑘,𝑙
𝑁+1
ℎ 𝑘
𝑁
+ 𝑏𝑙
𝑂
; 𝑦𝑙 = 𝑓 𝑠𝑙
𝑂
• Backward pass: correct weights to reduce error
 Gradient descentLayer Error Gradient
(w.r.t. weights between current and prev. layer)
Output Defined loss
(e.g. 𝐿 = 𝑖=1
𝑁 𝑜
𝑦𝑖 − 𝑦𝑖
2
)
𝜕𝐿
𝜕𝑤𝑗,𝑖
(𝑁+1)
=
𝜕𝐿
𝜕𝑦𝑖
∗
𝜕𝑦𝑖
𝜕𝑠𝑖
𝑂 ∗
𝜕𝑠𝑖
𝑂
𝜕𝑤𝑗,𝑖
𝑁+1 =
𝜕𝐿
𝜕𝑦𝑖
𝑓′
𝑠𝑖
𝑂
ℎ𝑗
𝑁
𝑁 𝑡ℎ
hidden
𝛿𝑖
𝑁
=
𝜕𝐿
𝜕𝑦𝑖
∗
𝜕𝑦𝑖
𝜕𝑠𝑖
𝑂
𝜕𝐿
𝜕𝑤 𝑘,𝑗
𝑁 =
𝑖
𝜕𝐿
𝜕𝑦𝑖
∗
𝜕𝑦𝑖
𝜕𝑠𝑖
𝑂 ∗
𝜕𝑠𝑖
𝑂
𝜕ℎ𝑗
𝑁 ∗
𝜕ℎ𝑗
𝑁
𝜕𝑠𝑗
𝑁 ∗
𝜕𝑠𝑗
𝑁
𝜕𝑤𝑗,𝑖
𝑁 =
𝑖
𝛿𝑖
𝑁
𝑤𝑖,𝑗
𝑁+1
𝑓′ 𝑠𝑗
𝑁
ℎ 𝑘
𝑁−1
(𝑁 −
𝛿𝑗
𝑁−1
=
𝑖
𝛿𝑖
𝑁
𝑤𝑖,𝑗
𝑁+1
𝑓′ 𝑠𝑗
𝑁
𝜕𝐿
𝜕𝑤𝑙,𝑘
𝑁−1 =
𝑗
𝛿𝑗
𝑁−1
𝑤𝑗,𝑘
𝑁
𝑓′ 𝑠 𝑘
𝑁−1
ℎ𝑙
𝑁−2
…
1 𝑠𝑡
hidden
𝛿 𝑘
1 𝜕𝐿
𝜕𝑤𝑖,𝑗
1 =
𝑘
𝛿 𝑘
1
𝑤 𝑘,𝑙
2
𝑓′
𝑠𝑗
1
𝑥𝑖
Challenges of training deep networks
• Saturation
• Vanishing gradients
• Overfitting
• Slowness of second order methods
• Slow convergence, stucks in local optima with first
order methods
• (Exploding gradients)
Why now?
Breakthroughs in research
• Saturation & vanishing gradients
 Layer-by-layer training (2006)
 Non-saturating activation functions, e.g. ReLU (2013)
• Overfitting
 Dropout (2014)
• Convergence problems
 Adagrad, Adadelta, Adam, RMSProp, etc.
Computational power
• Natural increase in computational power
• GP GPU technology
Intermission
Don’t give in to the HYPE
• Deep learning is impressive but
 deep learning is not true AI
o it may be a component of it when
and if AI is created
 deep learning is not how the human
brain works
 95% of machine learning tasks don’t
require deep learning
 deep learning requires a lot of
computational power
• Deep learning is a tool
 which is successful in certain,
previously very challenging domains
(speech recognition, computer
vision, NLP, etc.)
 that excels in pattern recognition
You are here
Deep learning for RecSys
From the Netflix prize...
• Netflix prize (2006-2009)
 Gave a huge push to recommender systems research
 Determined the direction of research for years
 Task:
o Some (User, Item, Rating) known triplets
o (User, Item) pairs with unknown rating
o Predict the missing ratings (1-5)
... to recommenders in practice
• Ratings  events [implicit feedback]
 Lots of services don’t allow for rating
 Majority of users don’t rate
 Monitored passively  preferences have to be infered
• Rating prediction  ranking [top N recommendations]
 All that matters is the relevancy of the top N items
 Rating prediction is biased
• User  session / situation [session-based / context-driven
recommendation]
 Users are not logged in, identification is unreliable
 Accounts used by multiple users
 Aim of the session (e.g. buy a good laptop)
 Similar behavior of different users in a situation, different behavior of the same
user in different situations
Challenges in RecSys
• Session modeling
 Most of the algorithms are personalized
 A few are item-to-item
o Recommends similar items
o Also used for session-based recommendations (industry de facto standard)
 There are no good session based solutions
• Incorporating factors that influence user clicks
 Users click based on what they see
o Title
o Product image
o Description
 and on their knowledge of the product
o Usually harder to model
o Except when the product is content (e.g. music)
Deep learning to the rescue – Session modeling
• Recurrent Neural Networks (RNN)
 Sequence modeling
 Hidden state: next state is based on the previous hidden state and the current input
 „Infinite” depth
 More sophisticated versions: GRU, LSTM
• Needs to be adapted to the recommendation task
• GRU4Rec:
 Session-parallel minibatch training for handling the large variance in session lengths
 Sampling the output for reasonable training times, without losing much accuracy
 Ranking loss for better item ranking
• Results: 15-30% improvement over item-to-item recommendations
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
RSC15 VIDEO
Recall@20
Item-kNN
GRU4Rec
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
RSC15 VIDEO
MRR@20
Item-kNN
GRU4Rec
Other uses of deep learning for recsys
• Incorporating content directly
 Music, images, video, text
 User influencing aspects of the items
 Direct content representation
• Context-state modeling from sensory data
 IoT devices
 Lot of sensory data
 Some missing and noise
 Infer context state and recommend accordingly
• Interactive recommenders using chatbots
• Personalized content generation
 Today’s news
 Images in personalized style with personalized content
• Etc...
There is work to be done
• DL + RecSys research: just started
 Last year:
o 0 long papers, 1 short paper and 1 poster that is loosely connected
 This year:
o 10+ submissions to RecSys in this topic
o DLRS 2016 workshop @ RecSys
• Open questions
 (More) Application areas
 Adaptations required for the recsys problem
 Scalability
 Best practices
 ...
Thanks for your attention!

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Deep learning: the future of recommendations

  • 1. Deep learning: the future of recommendations Balázs Hidasi Head of Data Mining and Research Gravity meetup @ Startup Safary April 21, 2016
  • 2. Deep learning in the headlines
  • 3. Deep learning in the background • Life improving services  Speech recognition  Personal assistants (e.g. Siri, Cortana)  Computer vision, object recognition  Machine translation  Chatbot technology  Natural Language Processing  Face recognition  Self driving cars • For fun  Text generation  Composing music  Painting pictures  Etc.
  • 4. What is deep learning? • A class of machine learning algorithms  that use a cascade of multiple non-linear processing layers  and complex model structures  to learn different representations of the data in each layer  where higher level features are derived from lower level features  to form a hierarchical representation.
  • 5. Deep learning is not a new topic • First deep network proposed in the 1970s • More papers in the 80s and 90s • Why now?  Older research was not used widely in practice  Applications were much more simplistic that today’s
  • 6. Neural networks: a brief overview
  • 7. Neurons, neural networks • Neuron: rough abstraction of the human neuron  Receives inputs (signals)  Sum weighted inputs is big enough  signal  Amplifiers and inhibitors  Basic pattern recognition • Neural network: neurons connected to one another • Feedforward networks: neurons are organized into layers  Connections only between subsequent layers 𝑦 𝑥1 𝑥2 𝑥3 𝑥4 𝑓(. ) 𝑖=1 𝑁 𝑤𝑖 𝑥𝑖 + 𝑏 𝑥1 𝑥2 𝑥3 ℎ1 1 ℎ2 1 ℎ3 1 ℎ1 2 ℎ2 2
  • 8. Networks that big enough: go deep not wide • Feedforward neural networks are universal approximators  Can imitate any function if they are big enough  (Also needs enough in-out pairs to learn) • What is big enough?  Number of layers / neurons  Theoretical „big enough” conditions massively overshoot • Go deep, not wide  The number of neurons required for good approximation is polynomial in the input if the network is deep enough  Otherwise it is exponential
  • 9. Training neural networks • Forward pass: get the current estimate of the target o 𝑠𝑗 1 = 𝑖 𝑤𝑖,𝑗 1 𝑥𝑖 + 𝑏𝑗 1 ; ℎ𝑗 1 = 𝑓 𝑠𝑗 1 o 𝑠 𝑘 2 = 𝑗 𝑤𝑗,𝑘 2 ℎ𝑗 1 + 𝑏 𝑘 2 ; ℎ 𝑘 2 = 𝑓 𝑠 𝑘 2 o … o 𝑠𝑙 𝑂 = 𝑘 𝑤 𝑘,𝑙 𝑁+1 ℎ 𝑘 𝑁 + 𝑏𝑙 𝑂 ; 𝑦𝑙 = 𝑓 𝑠𝑙 𝑂 • Backward pass: correct weights to reduce error  Gradient descentLayer Error Gradient (w.r.t. weights between current and prev. layer) Output Defined loss (e.g. 𝐿 = 𝑖=1 𝑁 𝑜 𝑦𝑖 − 𝑦𝑖 2 ) 𝜕𝐿 𝜕𝑤𝑗,𝑖 (𝑁+1) = 𝜕𝐿 𝜕𝑦𝑖 ∗ 𝜕𝑦𝑖 𝜕𝑠𝑖 𝑂 ∗ 𝜕𝑠𝑖 𝑂 𝜕𝑤𝑗,𝑖 𝑁+1 = 𝜕𝐿 𝜕𝑦𝑖 𝑓′ 𝑠𝑖 𝑂 ℎ𝑗 𝑁 𝑁 𝑡ℎ hidden 𝛿𝑖 𝑁 = 𝜕𝐿 𝜕𝑦𝑖 ∗ 𝜕𝑦𝑖 𝜕𝑠𝑖 𝑂 𝜕𝐿 𝜕𝑤 𝑘,𝑗 𝑁 = 𝑖 𝜕𝐿 𝜕𝑦𝑖 ∗ 𝜕𝑦𝑖 𝜕𝑠𝑖 𝑂 ∗ 𝜕𝑠𝑖 𝑂 𝜕ℎ𝑗 𝑁 ∗ 𝜕ℎ𝑗 𝑁 𝜕𝑠𝑗 𝑁 ∗ 𝜕𝑠𝑗 𝑁 𝜕𝑤𝑗,𝑖 𝑁 = 𝑖 𝛿𝑖 𝑁 𝑤𝑖,𝑗 𝑁+1 𝑓′ 𝑠𝑗 𝑁 ℎ 𝑘 𝑁−1 (𝑁 − 𝛿𝑗 𝑁−1 = 𝑖 𝛿𝑖 𝑁 𝑤𝑖,𝑗 𝑁+1 𝑓′ 𝑠𝑗 𝑁 𝜕𝐿 𝜕𝑤𝑙,𝑘 𝑁−1 = 𝑗 𝛿𝑗 𝑁−1 𝑤𝑗,𝑘 𝑁 𝑓′ 𝑠 𝑘 𝑁−1 ℎ𝑙 𝑁−2 … 1 𝑠𝑡 hidden 𝛿 𝑘 1 𝜕𝐿 𝜕𝑤𝑖,𝑗 1 = 𝑘 𝛿 𝑘 1 𝑤 𝑘,𝑙 2 𝑓′ 𝑠𝑗 1 𝑥𝑖
  • 10. Challenges of training deep networks • Saturation • Vanishing gradients • Overfitting • Slowness of second order methods • Slow convergence, stucks in local optima with first order methods • (Exploding gradients)
  • 12. Breakthroughs in research • Saturation & vanishing gradients  Layer-by-layer training (2006)  Non-saturating activation functions, e.g. ReLU (2013) • Overfitting  Dropout (2014) • Convergence problems  Adagrad, Adadelta, Adam, RMSProp, etc.
  • 13. Computational power • Natural increase in computational power • GP GPU technology
  • 15. Don’t give in to the HYPE • Deep learning is impressive but  deep learning is not true AI o it may be a component of it when and if AI is created  deep learning is not how the human brain works  95% of machine learning tasks don’t require deep learning  deep learning requires a lot of computational power • Deep learning is a tool  which is successful in certain, previously very challenging domains (speech recognition, computer vision, NLP, etc.)  that excels in pattern recognition You are here
  • 17. From the Netflix prize... • Netflix prize (2006-2009)  Gave a huge push to recommender systems research  Determined the direction of research for years  Task: o Some (User, Item, Rating) known triplets o (User, Item) pairs with unknown rating o Predict the missing ratings (1-5)
  • 18. ... to recommenders in practice • Ratings  events [implicit feedback]  Lots of services don’t allow for rating  Majority of users don’t rate  Monitored passively  preferences have to be infered • Rating prediction  ranking [top N recommendations]  All that matters is the relevancy of the top N items  Rating prediction is biased • User  session / situation [session-based / context-driven recommendation]  Users are not logged in, identification is unreliable  Accounts used by multiple users  Aim of the session (e.g. buy a good laptop)  Similar behavior of different users in a situation, different behavior of the same user in different situations
  • 19. Challenges in RecSys • Session modeling  Most of the algorithms are personalized  A few are item-to-item o Recommends similar items o Also used for session-based recommendations (industry de facto standard)  There are no good session based solutions • Incorporating factors that influence user clicks  Users click based on what they see o Title o Product image o Description  and on their knowledge of the product o Usually harder to model o Except when the product is content (e.g. music)
  • 20. Deep learning to the rescue – Session modeling • Recurrent Neural Networks (RNN)  Sequence modeling  Hidden state: next state is based on the previous hidden state and the current input  „Infinite” depth  More sophisticated versions: GRU, LSTM • Needs to be adapted to the recommendation task • GRU4Rec:  Session-parallel minibatch training for handling the large variance in session lengths  Sampling the output for reasonable training times, without losing much accuracy  Ranking loss for better item ranking • Results: 15-30% improvement over item-to-item recommendations 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 RSC15 VIDEO Recall@20 Item-kNN GRU4Rec 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 RSC15 VIDEO MRR@20 Item-kNN GRU4Rec
  • 21. Other uses of deep learning for recsys • Incorporating content directly  Music, images, video, text  User influencing aspects of the items  Direct content representation • Context-state modeling from sensory data  IoT devices  Lot of sensory data  Some missing and noise  Infer context state and recommend accordingly • Interactive recommenders using chatbots • Personalized content generation  Today’s news  Images in personalized style with personalized content • Etc...
  • 22. There is work to be done • DL + RecSys research: just started  Last year: o 0 long papers, 1 short paper and 1 poster that is loosely connected  This year: o 10+ submissions to RecSys in this topic o DLRS 2016 workshop @ RecSys • Open questions  (More) Application areas  Adaptations required for the recsys problem  Scalability  Best practices  ...
  • 23. Thanks for your attention!