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)

11. Why now?

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

14. Intermission

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

16. Deep learning for RecSys

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!