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Deep Recommender Systems - PAPIs.io LATAM 2018
- 1. Deep Recommender Systems Gabriel Moreira • @gspmoreira LatAm 2018 Lead Data Scientist Phd. Candidate
- 2. Introduction "We are leaving the Information Age and entering the Recommendation Age.". Cris Anderson, "The long tail" 2
- 3. Introduction 37% of sales 2/3 watched movies 38% of top news visualization Recommender Systems are responsible for: 3
- 4. Recommender System Methods 4
- 5. Collaborative Filtering based on Matrix Factorization
- 6. Going deeper...
- 7. Why Deep Learning has a potential for RecSys? ● Feature extraction directly from the content (e.g., image, text, audio) ● Heterogenous data handled easily ● Dynamic behaviour modeling with RNNs ● More accurate representation learning of users and items ○ Natural extensions of CF ● RecSys is a complex domain ○ Deep learning worked well in other complex domains
- 8. The Deep Learning era of RecSys 2007 2015 2016 2017-2018 Deep Boltzmann Machines for rating prediction calm before the storm A few seminal papers First DLRS workshop and papers on RecSys, KDD, SIGIR Continued increase
- 9. Research directions in DL-RecSys And their combinations... Session-based recommendations with RNNs Feature Extraction directly from the content Learning Item embeddings Deep Collaborative Filtering
- 10. Learning Item Embeddings
- 11. Item embeddings ● Embedding: a (learned) real value vector representing an entity ● Also known as Latent feature vector / (Latent) representation ● Similar entities’ embeddings are similar ● Use in recommenders: ○ Initialization of item representation in more advanced algorithms ● Item-to-item recommendations Learning Item Embeddings
- 12. Prod2Vec (Grbovic et. al, 2015) ● Based on word2vec and paragraph2vec ● Skip-gram model on products ○ Input: i-th product purchased by the user ○ Context: the other purchases of the user ● Learning user representation ○ Follows paragraph2vec ○ User embedding added as global context ○ Input: user + products purchased except for the i-th ○ Target: i-th product purchased by the user User embeddings for user to produce predictions prod2vec skip-gram model Learning Item Embeddings
- 13. Feature extraction from content for Recommender Systems
- 14. Feature extraction from unstructured data Images Text Audio/Music ● CNN ● 1D CNN ● RNNs ● Weighted word embeddings ● CNN ● RNN
- 15. ● Initializing ○ Obtain item representation based on metadata ○ Use this representation as initial item features ● Regularizing ○ Obtain metadata based representations ○ The interaction based representation should be close to the metadata based (regularizing term to loss of this difference) ● Joining ○ o Have the item feature vector be a concatenation ■ Fixed metadata based part ■ Learned interaction based part Content Features in Hybrid Recommenders
- 16. Wide & Deep Learning
- 17. Wide & Deep Learning (Cheng et. al, 2016) ● Joint training of two models ○ Deep Neural Network - Focused in generalization ○ Linear Model - Focused in memorization ● Improved online performance ○ +2.9% deep over wide ○ +3.9% deep & wide over wide Deep Collaborative Filtering
- 18. Outbrain Click Prediction - Kaggle competition Dataset ● Sample of users page views and clicks during 14 days on June, 2016 ● 2 Billion page views ● 17 million click records ● 700 Million unique users ● 560 sites Can you predict which recommended content each user will click? 18 Wide & Deep Model example
- 19. Wide & Deep Model example Outbrain Click Prediction - Data Model Numerical Spatial Temporal Categorical Target
- 20. Wide & Deep Model example Source: https://github.com/gabrielspmoreira/kaggle_outbrain_click_prediction_google_cloud_ml_engine DNNLinearCombinedClassifier estimator
- 21. Wide & Deep Model example Source: https://github.com/gabrielspmoreira/kaggle_outbrain_click_prediction_google_cloud_ml_engine Wide and Deep features
- 22. Wide & Deep Model example Source: https://github.com/gabrielspmoreira/kaggle_outbrain_click_prediction_google_cloud_ml_engine POC Results Framework / Platform Model Mean Average Precision (MAP) Vowpal Wabbit Linear 0.6751 Tensorflow / Google ML Engine Linear (Wide) 0.6779 Deep model 0.6674 Wide & Deep model 0.6765
- 23. Source: https://stuartcove.com/ Going even deeper...
- 24. News Recommender Systems using Deep Learning
- 25. News Recommender Systems News RS Challenges ● Sparse user profiling (LI et al. , 2011) (LIN et al. , 2014) (PELÁEZ et al. , 2016) ● Fast growing number of items (PELÁEZ et al. , 2016) (MOHALLICK; ÖZGÖBEK , 2017) ● Accelerated item’s value decay (DAS et al. , 2007) ● Users’ preferences shift (PELÁEZ et al. , 2016) (EPURE et al. , 2017)
- 26. To investigate, design, implement, and evaluate a deep learning meta-architecture for news recommendation, in order to improve the accuracy of recommendations provided by news portals, satisfying readers' dynamic information needs in such a challenging recommendation scenario. Research Objective
- 27. Conceptual model of factors affecting news relevance News relevance Topics Entities Publisher News static properties Recency Popularity News dynamic properties News article User TimeLocation Device User current context Long-term interests Short-term interests Global factors Season- ality User interests Breaking events Popular Topics Referrer
- 28. Recency factor influence Article age (days) Clicks
- 29. Meta-Architecture Requirements ● RQ1 - to provide personalized news recommendations in extreme cold-start scenarios, as most news are fresh and most users cannot be identified ● RQ2 - to use Deep Learning to automatically learn news representations from textual content and news metadata, minimizing the need of manual feature engineering ● RQ3 - to leverage the user session information, as the sequence of interacted news may indicate the user’s short-term preferences for session-based recommendations ● RQ4 - to leverage user's past sessions information, when available, to model long-term interests for session-aware recommendations
- 30. Meta-Architecture Requirements ● RQ5 - to leverage users’ contextual information as a rich data source, in such information scarcity about the user ● RQ6 - to model explicitly contextual news properties – popularity and recency – as those are important factors on news interest life cycle ● RQ7 - to support an increasing number of new items and users by incremental model retraining (online learning), without the need to retrain on the whole historical dataset ● RQ8 - to provide a modular structure for news recommendation, allowing its modules to be instantiated by different and increasingly advanced neural network architectures and methods
- 31. CHAMELEON Meta-Architecture for News RS Article Context Article Content Embeddings Article Content Representation (ACR) Textual Features Representation (TFR) Metadata Prediction (MP) Category Tags Entities Article Metadata Attributes Next-Article Recommendation (NAR) Time Location Device When a news article is published... User context User interaction past read articles Popularity Recency Article context Users Past Sessions Article Content Embedding candidate next articles (positive and neg.) active article Active Sessions When a user reads a news article... Predicted Next-Article Embedding Session Representation (SR) Recommendations Ranking (RR) User-Personalized Contextual Article Embedding Recommended articles Contextual Article Representation (CAR) Content word embeddings New York is a multicultural city , ...Publisher Metadata Attributes News Article Active user session Module Sub-Module EmbeddingInput Output Data repositoryAttributes Article Content Embedding Legend: Word Embeddings
- 32. CHAMELEON - ACR module Article Content Embeddings Article Content Representation (ACR) Textual Features Representation (TFR) Metadata Prediction (MP) Category Tags Entities Article Metadata Attributes When a news article is published... Content word embeddings New York is a multicultural city , ...Publisher Metadata Attributes News Article Module Sub-Module EmbeddingInput Output Data repositoryAttributes Article Content Embedding Legend: Word Embeddings T-SNE viz. of articles embeddings colored by category, with similar articles highlighted “Sports” “Berlin”
- 33. Articles Embeddings Similarity Evaluation (e.g. “sports”) Article Content (Title | Kicker | Description) Similarity Kommentar: Generation Mut | Kommentar von Alexander Wölffing zur Handball-Nationalmannschaft bei der EM | Der offensive Ansatz des Deutschen Handball-Bundes um Bob Hanning zahlt sich aus. Alexander Wölffing, stellvertretender Chefredakteur und Leiter Sports bei SPORT1, kommentiert. - ”Seahawks werden Brady bearbeiten” | NFL-Legende Hines Ward analysiert den Super Bowl | Hines Ward favorisiert Seattle. Der frühere MVP des Endspiels sieht aber Vollmer und Co. sowie einen X-Faktor als Chance der Patriots. Für SPORT1 analysiert er den Super Bowl. 0.970 Bald kein ”Hack-a-Drummond” mehr? | Foul als taktisches Mittel: NBA will ”hack-a-player”-Regel ¨ander | Andre rummond und alle schlechten Freiwerfer in der NBA wird es freuen: Die Liga will Fouls als taktisches Mittel anders bestrafen, um den Sport nicht kaputt zu machen. 0.964 Nummer 2: Leno findet sich damit ab | Bernd Leno spricht im SPORT1- Interview uber Manuel Neuer ¨ | Bayer Leverkusens Bernd Leno nimmt bei SPORT1 Stellung zur Torhuter-Situation im DFB-Team sowie Vorbild ¨ Manuel Neuer - und spricht auch uber Fehler und Frust. 0.962 33
- 34. CHAMELEON - NAR module Article Context Article Content Embeddings Next-Article Recommendation (NAR) Time Location Device User context User interaction past read articles Popularity Recency Article context Users Past Sessions candidate next articles (positive and neg.) active article Active Sessions When a user reads a news article... Predicted Next-Article Embedding Session Representation (SR) Recommendations Ranking (RR) User-Personalized Contextual Article Embedding Recommended articles Contextual Article Representation (CAR) Active user session Module Sub-Module EmbeddingInput Output Data repositoryAttributesLegend: Article Content Embedding 34
- 35. CHAMELEON - NAR module Article Context Article Content Embeddings Next-Article Recommendation (NAR) Time Location Device User context User interaction past read articles Popularity Recency Article context Users Past Sessions candidate next articles (positive and neg.) active article Active Sessions When a user reads a news article... Predicted Next-Article Embedding Session Representation (SR) Recommendations Ranking (RR) User-Personalized Contextual Article Embedding Recommended articles Contextual Article Representation (CAR) Active user session Module Sub-Module EmbeddingInput Output Data repositoryAttributesLegend: Sessions in a batch Article Content Embedding 35 I1,1 I1,2 I1,3 I1,4 I1,5 I2,1 I2,2 I3,1 I3,2 I3,3 Input I1,1 I1,2 I1,3 I1,4 I1,2 I1,3 I1,4 I1,5 Expected Output (labels)
- 36. CHAMELEON - NAR module Article Context Article Content Embeddings Next-Article Recommendation (NAR) Time Location Device User context User interaction past read articles Popularity Recency Article context Users Past Sessions candidate next articles (positive and neg.) active article Active Sessions When a user reads a news article... Predicted Next-Article Embedding Session Representation (SR) Recommendations Ranking (RR) User-Personalized Contextual Article Embedding Recommended articles Contextual Article Representation (CAR) Active user session Module Sub-Module EmbeddingInput Output Data repositoryAttributesLegend: Article Content Embedding 36 Negative Sampling strategy Articles read by users overall n the last hour Buffer Articles read in other user sessions in the batch Next article read by user in his session Samples
- 37. CHAMELEON - NAR module Article Context Article Content Embeddings Next-Article Recommendation (NAR) Time Location Device User context User interaction past read articles Popularity Recency Article context Users Past Sessions candidate next articles (positive and neg.) active article Active Sessions When a user reads a news article... Predicted Next-Article Embedding Session Representation (SR) Recommendations Ranking (RR) User-Personalized Contextual Article Embedding Recommended articles Contextual Article Representation (CAR) Active user session Module Sub-Module EmbeddingInput Output Data repositoryAttributesLegend: Article Content Embedding 37 Recommendations Ranking (RR) sub-module Eq. 7 - Loss function (HUANG et al., 2013) Eq. 4 - Relevance Score of an item for a user session Eq. 5 - Cosine similarity Eq. 6 - Softmax over Relevance Score (HUANG et al., 2013)
- 38. CHAMELEON - NAR module loss function Recommendation loss function implemented on TensorFlow
- 39. ● Predict the next article user will read in a session ● Training with interactions up to an hour, and evaluate the predictions of next-item clicks for the next hour sessions ● Ranking Metrics ○ Recall@3 - Scores when the actually clicked article is among the top-3 items in the recommended ranking list ○ NDCG@3 - Takes into account the predicted position of actually clicked item (e.g. in position 1 it would score higher than on position 2) ● Evaluation Benchmarks ○ Popular Recent - Keeps a buffer of all clicks of the last hour, and return the most popular articles on that time window ○ Co-occurrent - Recommends articles that are most commonly read together on user sessions ○ Content-Based - Return articles whose content embeddings are the most similar CHAMELEON - Offline evaluation protocol
- 40. CHAMELEON - Preliminary Results Recall@3 distribution over time
- 41. CHAMELEON - Preliminary Results NDCG@3 distribution over time
- 42. Recall@3 distribution CHAMELEON - Preliminary Results 60,5% 66,5% 57%
- 43. CHAMELEON - Preliminary Results Recall@3 by hour
- 44. CHAMELEON - Preliminary Results Recall@3 by day
- 45. ● ACR module ○ Try different word embeddings (Word2Vec, GloVe) ○ Try different textual feature extractors (CNNs, RNNs) to generate articles embeddings ● NAR module ○ Test different RNN architectures (LSTM, GRU) ○ Try different negative sampling strategies ○ Leverage users past sessions to initialize RNNs for new sessions ○ Evaluate on different (and larger) news datasets ○ Hyperparameters tuning Next steps
- 46. https://www.slideshare.net/balazshidasi/deep-learning-in-recommender-systems-recsys-summer-school-2017 References
- 47. https://github.com/datadynamo/strata_ny_2017_recommender_tutorial Resources
- 48. Resources Deskdrop dataset shared on Kaggle bit.ly/dataset4recsys ● 12 months logs (Mar. 2016 - Feb. 2017) ● ~ 73k user interactions ● ~ 3k shared articles in the platform
- 49. Gabriel Moreira Lead Data Scientist gabrielpm@ciandt.com @gspmoreira We are hiring! http://bit.ly/ds4ciandt
