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Highlights on most interesting RecSys papers - Elena Smirnova, Lowik Chanussot, Amine Benhalloum - Criteo

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RecSys conference was held in Como at the end of August. We will summarize for you the most trendy techniques and results presented at this conference.

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Highlights on most interesting RecSys papers - Elena Smirnova, Lowik Chanussot, Amine Benhalloum - Criteo

  1. 1. Copyright © 2017 Criteo Highlights of RecSys'17 Elena Smirnova, Amine Benhalloum, Lowik Chanussot Criteo 25/09/2017
  2. 2. Copyright © 2017 Criteo Introduction • RecSys'17 was held in Como, Italy on August, 27-31 • +600 participants • Increasing number of industry sessions • Criteo presented 2 papers on Deep learning workshop
  3. 3. Copyright © 2017 Criteo RecSys'17: Key topics • Session-based recommendation: Elena • Representation learning: Amine • Scalability: Lowik
  4. 4. Copyright © 2017 Criteo Session-based recommendation
  5. 5. Copyright © 2017 Criteo Session-based recommendation Classical setup: independent user-item observations link prediction matrix factorization New setup: sequences of user-item interactions in time next event prediction Time
  6. 6. Copyright © 2017 Criteo Recurrent Neural Networks RNNs for session-based recommendation introduced in 2015 Learns sequence embedding (aka internal state) that represents the sequence of user-item interactions Performs the same computation at each time step Hidasi et al. Session-based Recommendations with Recurrent Neural Networks.
  7. 7. Copyright © 2017 Criteo RecSys’17: Stronger baselines Session-based kNN • Find k most similar past sessions • Cosine similarity of bit vectors • Score items by the sum of session similarities D. Jannach and M. Ludewig. When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation.
  8. 8. Copyright © 2017 Criteo RecSys’17: Hierarchical Extension Hierarchical RNNs model long-term user behavior across sessions 2 RNNs: user and session representation M. Quadrana et al. PersonalizingSession-basedRecommendations with Hierarchical RecurrentNeuralNetworks.
  9. 9. Copyright © 2017 Criteo RecSys’17: Contextual Extension Condition RNN on contextual information (event type, timestamp) Integrate at 2 levels: Input and Output layers Hidden Dynamics E. Smirnova and F. Vasile. Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks.
  10. 10. Copyright © 2017 Criteo Wrap up • Session-based recommendation has now it own track • Stronger baselines have been introduced • Multiple extensions to Recurrent Neural Networks to better model user behavior
  11. 11. Copyright © 2017 Criteo Representation learning
  12. 12. Copyright © 2017 Criteo Representation learning Learning to represent items, users and their relationships in an appropriate space (as a real valued vector)
  13. 13. Copyright © 2017 Criteo Representation learning Leveraging available content (images, descriptions, reviews …) Helping with the cold start problem
  14. 14. Copyright © 2017 Criteo Leveraging content: Review texts R. Catherine et al. Transnets: Learning to transform for recommendation • Review texts are available, how can we use them ? • Learn a representation of the review and then predict the associated rating
  15. 15. Copyright © 2017 Criteo Leveraging content: Item features T. Nedelec et al. : Content2Vec: Specializing jointrepresentationsof productimagesand text for the task of product recommendation • How can we combine heterogeneous product representations ? • Specialize feature representations (text, image, …) for the task of item-item similarity and merge them
  16. 16. Copyright © 2017 Criteo Cold start problem: Attribute to feature mapping • Can we use item characteristics to initialize an new item's latent representation ? • Learning attribute (item characteristics) to feature (latent space representation) mapping. D. Cohen et al. : Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendation
  17. 17. Copyright © 2017 Criteo Cold start problem: Attribute to feature mapping • We learn a linear mapping between attribute vectors {𝒂𝒊} and latent representation {𝒗𝒊} 𝑣𝑖 ≈ 𝑊𝑎𝑖 • For a new item 𝒋 we initialize its latent representation 𝑣𝑗 0 ≈ 𝑊𝑎𝑗 D.Cohen et al. : Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendation
  18. 18. Copyright © 2017 Criteo Folding • “Folding” effect of embedding can lead to spurious recommendations • Model has to take into account data Missing Not At Random, introduce metric to measure the severity of folding D. Xin et al. Folding: Why Good Models Sometimes Make Spurious Recommendations.
  19. 19. Copyright © 2017 Criteo Wrap up • Embed all the things ! • A Deep MultimodalApproach for Cold-start Music Recommendation(Oramas et al.) • Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation (Dominguez et al.) • Translation based recommendation (He et al.) • Use content and reviews • InterpretableConvolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction (Seo et al.) • Recommendation of High Quality Representative Reviews in e-commerce (Paul et al.) • Sequential recommendations • Sequential User-based Recurrent Neural Network Recommendations (Donkers et al.)
  20. 20. Copyright © 2017 Criteo Scalability
  21. 21. Copyright © 2017 Criteo Large scale constraints Many products, many users Online latency Training time Offline storage
  22. 22. Copyright © 2017 Criteo Convolution at character level for session-based reco Id Name Category Tuan et al. 3D Convolutional Networks for Session-based Recommendation with Content Features Item #1 Item #2 Item #n view view basket
  23. 23. Copyright © 2017 Criteo Convolution at character level for session-based reco Id Name Category 0 a b c d … 1 2 3 4 5 6e …@? ! 1 1 1 Id: 263N Tuan et al. 3D Convolutional Networks for Session-based Recommendation with Content Features Item #1 Item #2 view view basket Item #n
  24. 24. Copyright © 2017 Criteo Convolution at character level for session-based reco Id Name Category 0 a b c d … 1 2 3 4 5 6e …@? ! 1 1 1 Id: 263 Name: “iPhone” Category: “Phones/Apple” V N N x 3V Item #1 Tuan et al. 3D Convolutional Networks for Session-based Recommendation with Content Features Item #1 Item #2 view view basket Item #n
  25. 25. Copyright © 2017 Criteo Convolution at character level for session-based reco Item #1 Item #2 Id Name Category 0 a b c d … 1 2 3 4 5 6e …@? ! 1 1 1 3*V = 56 N=150 Compact Input 3V x N x D Id: 263 Name: “iPhone” Category: “Phones/Apple” V N N x 3V Item #1 Tuan et al. 3D Convolutional Networks for Session-based Recommendation with Content Features view view basket Item #n
  26. 26. Copyright © 2017 Criteo Convolution at character level for session-based reco Tuan et al. 3D Convolutional Networks for Session-based Recommendation with Content Features
  27. 27. Copyright © 2017 Criteo Scaling deep nets Your favorite deep net D x L1 D x Ln - Compact - Fast to compute - For input and output - Reversible output - No change in the deep-net - Appropriate loss - Same accuracy Serrà et al. Getting Deep Recommenders Fit : Bloom Embeddings for Sparse Binary Input / Output Networks
  28. 28. Copyright © 2017 Criteo Bloom filters embeddings 1 1 1 H1 H2 Hk 1 D m < D p Serrà et al. Getting Deep Recommenders Fit : Bloom Embeddings for Sparse Binary Input / Output Networks
  29. 29. Copyright © 2017 Criteo Bloom filters embeddings 1 1 1 H1 H2 Hk 1 Your favorite deep net 0.1 0.3 0.2 D m < D m x L1 m x Ln p v Serrà et al. Getting Deep Recommenders Fit : Bloom Embeddings for Sparse Binary Input / Output Networks
  30. 30. Copyright © 2017 Criteo Bloom filters embeddings 1 1 1 H1 H2 Hk 1 Your favorite deep net 0.1 0.3 0.2 D m < D m x L1 m x Ln 1 q p H1 Hk y v H2 Serrà et al. Getting Deep Recommenders Fit : Bloom Embeddings for Sparse Binary Input / Output Networks
  31. 31. Copyright © 2017 Criteo Wrap up Dedicated workshop Preselection of products Scaling deep nets Online Ranking Candidates Items Offline Selection N products K products (K << N)
  32. 32. Copyright © 2017 Criteo Conclusion RecSys’18 - Vancouver, Canada, 2nd-7th October 2018
  33. 33. Copyright © 2017 Criteo Thank you!
  34. 34. Copyright © 2017 Criteo Join Criteo!

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