Embeddings for recommendation systems
- 1. Embeddings for Recommendation Systems Keynote Speech Sihem Romdhani – Data Scientist
- 2. People watch one billion hours of YouTube every day YouTube’s recommendations drive 70% of what we watch = 700.000.000 hours of video / day YouTube generates 6% of Google’s ad sales revenue.
- 3. Digital Marketing and Recommender Systems Individualized customer experience Higher Traffic & Increased revenue Product Bundling Engaging Shoppers Real-Time Recommendations AI-based Recommender Systems Machine Learning Models for predicting product preference. Recommender Systems help us to manage big amounts of customer data, and to extract preferences on the individual customers‘ level.
- 4. AI-based Recommender Systems Approache s Collaborative filtering Content-based filtering Embeddings
- 6. Background On Embeddings in NLP
- 7. Word Embeddings Feature Vector representations of words (i.e. representing text as numbers) Capture semantic and syntactic relationships.
- 8. Word Embeddings Feature Vector representations of words (i.e. representing text as numbers) Capture semantic and syntactic relationships. 0.1 0.67 - -- 0.5 0.8 - -- 0.01 0.9 - -- king man woma n queen 0.6 0.19 - -- Word embedding (e.g. word2vec, GloVe) Similarity( 0.1 0.67 - -- 0.5 0.8 - -- )=0.8 king man ,
- 9. Word2vec and Skipgram for Learning Word Embeddings 0.8 0.01 … 0.89Marketing Sale School art … … …. advertising Embedding Size Embeddings “You shall know a word by the company it keeps” J.R. Firth Random Initialization1
- 10. Word2vec and Skipgram for Learning Word Embeddings Untrained Model Task: Are the two words neighbors? 0.8 0.01 … 0.89Marketing Sale School art … … …. advertising Embedding Size Embeddings “You shall know a word by the company it keeps” J.R. Firth Random Initialization1 Look up embeddings2 Marketing school Train the model3 Model Prediction 4 0.45 0.55 Yes No
- 11. Word2vec and Skipgram for Learning Word Embeddings Untrained Model Task: Are the two words neighbors? 0.8 0.01 … 0.89Marketing Sale School art … … …. advertising Embedding Size Embeddings “You shall know a word by the company it keeps” J.R. Firth Random Initialization1 Look up embeddings2 Marketing school Train the model3 Model Prediction 4 0.45 0.55 Yes No Actual Target & Error Estimation 5 0 1 Yes No Update Model Parameters
- 12. Word2vec and Skipgram for Learning Word Embeddings Marketing is the study and management of exchange relationships. Source: Wikipedia Sliding window across running text context Input word Output word Target is marketing 1 is the 1 Dataset
- 13. Word2vec and Skipgram for Learning Word Embeddings Marketing is the study and management of exchange relationships. Source: Wikipedia Sliding window across running text context Input word Output word Target is marketing 1 is the 1 the is 1 the study 1 Dataset
- 14. Word2vec and Skipgram for Learning Word Embeddings Dataset Marketing is the study and management of exchange relationships. Source: Wikipedia Input word Output word Target is marketing 1 is school 0 is the 1 is art 0 the is 1 the cat 0 the study 1 Negative Sampling
- 17. Researchers from the Web Search, E- commerce and Marketplace domains have realized that just like one can train word embeddings by treating a sequence of words in a sentence as context, the same can be done for training embeddings of user actions by treating sequence of user actions as context. Applying word2vec to Recommenders and Advertising Mihajlo Grbovic, Airbnb User activity around an item encodes many abstract qualities of that item which are difficult to capture by more direct means (e.g. How do you encode qualities like “architecture, style and feel” of an Airbnb listing? The word2vec approach has proven successful in extracting these hidden insights. Being able to compare, search, and categorize items on these abstract dimensions opens up a lot of opportunities for smarter, better recommendations.
- 19. Airbnb Recommender Systems Airbnb is a marketplace that contains millions of diverse home listings Potential guests explore the listings through search results generated from a sophisticated Machine Learning models. Similar Listing Carousel shows listing recommendations related to the current viewed listing. Initially, search rankings were determined by a set of hard-coded rules based on very basic signals The rules were applied to every guest uniformly, rather than taking into account the unique values that could create the kind of a personalized experience that keeps guests coming back. Airbnb uses machine learning to offer personalized search experience.
- 20. Listing Embeddings in Search Ranking Airbnb Recommender Systems The listing embeddings are vector representations of Airbnb homes learned from search sessions. They effectively encode many listing features, such as location, price, listing type, architecture and listing style, all using only 32 float numbers. Measure similarities: Similar listings lie nearby in the embedding space. Improving Similar Listing Recommendations and Real-Time Personalization in Search Ranking.
- 25. Click sessions Listing #100 Listing #200 Listing #300 Listing #400 Listing #500 Listing #600 Airbnb Listing Embeddings Listing #111 Listing #2900 Listing #415 Listing #100 Listing #200 Listing #300 Listing #400 Listing #500 Listing #600 Listing #700 Listing #800 Listing #900 Listing #1000
- 27. Airbnb Listing Embeddings Listing #100 Listing #200 Listing #300 Listing #400 Listing #500 Listing #600 Listing #700 …. Input listing Output listing class #200 #100 1 #200 #300 1 #300 #200 1 #300 #400 1 … … …
- 28. Airbnb Listing Embeddings Listing #100 Listing #200 Listing #300 Listing #400 Listing #500 Listing #600 Listing #700 …. Input listing Output listing class #200 #100 1 #200 #300 1 #300 #200 1 #300 #400 1 … … …
- 29. Airbnb Listing Embeddings Listing #100 Listing #200 Listing #300 Listing #400 Listing #500 Listing #600 Listing #700 …. Input listing Output listing class #200 #100 1 #200 #71 0 #200 #300 1 #200 #417 0 #300 #200 1 #300 #33 0 #300 #400 1
- 30. 0.06 0.1 … 0.47Listing #1 Listing #2 Listing #3 Listing #4 Listing #5 Listing #6 …. Listing #1000 Vector of 34 floating numbers Listing Embeddings Airbnb Listing Embeddings
- 31. 0.06 0.1 … 0.47Listing #1 Listing #2 Listing #3 Listing #4 Listing #5 Listing #6 …. Listing #1000 Listing #3 Most Similar Listings Listing #72 Listing #2006 Listing #1345 Listing #491 Listing #304 Vector of 34 floating numbers Listing Embeddings recommend Click Airbnb Listing Embeddings
- 32. New Labeled Data Most Similar Listings Listing #72 Listing #2006 Listing #1345 Listing #491 Listing #304 After recommending Clicked Clicked Not clicked Listing #72 Listing #2006 Listing #1345 Recommended Input listing Output listing label #3 #1345 0 Improving Recommendations Listing #3 Click
- 33. Improving Recommendations booked Input listing Output listing label #200 #100 1 #200 #300 1 #200 #1000 1 #300 #1000 1 Click sessions Listing #100 Listing #200 Listing #300 Listing #400 Listing #500 Listing #600 Listing #111 Listing #2900 Listing #415 Listing #100 Listing #200 Listing #300 Listing #400 Listing #500 Listing #600 Listing #700 Listing #800 Listing #900 Listing #1000 New Labeled Data
- 35. Challenges
- 36. ML systems are not good at generalizing when the underlying data distribution changes. What is needed to build a ML product What’s needed to build a supervised learning model Robustness in AI
- 37. Covid-19 Pandemic… Sudden change in Consumers' behavior New behaviors derail old recommender engines, AI inventory trackers, and fraud detection systems
- 38. Building Robust Recommender Systems The AI community needs to implement new approaches and processes for post-deployment monitoring like: building an alert system to flag changes, use human-in-the-loop deployments to acquire new labels, assemble a robust MLOps team. Valuable product resilient to conditions change
- 39. Toward Ethical AI… Recommender Systems Vs. Data Privacy
- 40. How to build recommender systems without compromising data privacy? Building the foundation for a responsible and innovative Data Economy Secure computation (e.g. cryptography). Secure hardware usage. Transparency and Audit. ML Models that can learn from small datasets
- 41. @ICDEc sihemromdhani @Sihem_Romdhani ICDEc Virtual Conference 2020 romdhani.sihem@gmail.com Sihem Romdhani Thank you