Machine Learning + Graph Databases for Better Recommendations V1 08/06/2022
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Note: You have to download the slides and use either powerpoint or google slides to make the links clickable. Machine Learning + Graph Databases for Better Recommendations Presented by Chris Woodward
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Machine Learning + Graph Databases for Better Recommendations V1 08/06/2022
- 1. 1 Machine Learning + Graph Databases for Better Recommendations
- 2. 2 Chris Woodward Developer Relations Engineer @ArangoDB â Training â Development â Community â ArangoML â Twitter: @cw00dw0rd â Slack: Chris.ArangoDB â Email: chris@arangodb.com
- 3. 3 The Agenda âArangoFlix Project âGraph Database âRecommendations âMachine Learning Techniques â Collaborative Filtering with AQL â Content-based Recommendations with ArangoSearch and TFIDF â Content-based Recommendations with FAISS, TFIDF, and Python â Matrix Factorization â Graph Neural Networks with PyTorch âGraph Database + ML
- 4. 4 What is ArangoFlix? âMachine Learning + Graph Databases âArangoDB Oasis âArangoFlix Website
- 5. 5 What and Why of Graph Making Relationships a First Class Citizen â ArangoDB turns the value of data relationships into actionable results â Data relationships are the foundation of AI/ML models SQL DB Product 1 Price Category Description e.g. Product Listing Product 2 Price Category Description Product 3 Price Category Description Graph/NoSQL DB e.g. Co-Purchase Pattern Product 2 Product 4 Product 1 Product 3 Product 4 Price Category Description Rather than focus on individual rows or products⊠Graph DB captures dependencies and relationships between those products
- 6. 6 Graph Database âCollection of nodes and edges âNaturally describes relations in data âFeasibly handles large joins/traversals âBuilt-in graph algorithms (K paths, shortest path, etc) âUse Cases: â Fraud Detection â Supply Chain Management â Recommendations â Customer 360 â Network Management â Risk Management
- 7. 7 ML + Graph Databases GraphDB ML Ecosystem ⊠GraphQL Data Ecosystem Knowledge Graph MetaData Graph Analytics GraphML Inferences Embeddings/ Inferences Graph data DGL, PyG, NetworkX,... Cloud
- 8. 8 What is a Recommendation System?
- 9. 9 âProvides predictions to business/users âBusiness Driven âData Quality âPrivacy/explainability Considerations âDomain Specific âImplementation Methods â Content-Based â Collaborative Filtering â Hybrid/Group/Other Recommendation System/Engine
- 10. 10 Recommendation System - Use Cases Domains âProducts âJobs âDestinations âPeople âEntertainment âResearch âSearch Companies âAmazon, Newegg, Instacart âGlassDoor, Indeed âAirBnB, Maps, Kayak âReddit, LinkedIn, Twitter âNetflix, Xbox, AppleTV âHealthcare, Citation âGoogle, Bing, (Page Rank)
- 11. 11 ⊠Customer LOB Applications DATA DATA LAKE Big Data Processing ETL Business Rules OPERATIONAL DATA Knowledge Graph DATA WAREHOUSE Deploy Monitor Intelligent App Feature Store ML Metadata Machine Learning ML Engineer Data Scientist MODELS Data Analyst Data Engineer Developer ETL ETL Data Movement within Organizations
- 12. 12 Application Backend/Storage Recommendation Logic Customer Simplified Recommendation Flow
- 13. 13 ArangoFlix - Demo Site ArangoDB Cloud https://cloud.arangodb.com Examples > Install > Demo https://flix.arangodb.com
- 14. 14 Stack Recommendations âArangoSearch & AQL âTFIDF - Content Based â Sklearn âMatrix Factorization - Collaborative Filtering â Surprise/SVD âGNN â PyTorch Geometric â sBert Backend âArangoDB Cloud â Driver: python-arango âFoxx Microservices â GraphQL Endpoint Frontend âVueJS / VueX âCytoscape âPrimeVue
- 16. 16 Content-based Filtering â Very personalized recommendation â Uses existing data to offer predictions â Typically requires domain knowledge â Can be fast and ad-hoc Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback. - Google
- 17. 17 TFIDF Term Frequency: How often the word shows up in a document. Inverse Document Frequency: How often the word shows up across all documents. Attempts to rank information based on the quality of the words, not just the frequency. tfidf(t, d, D) = tf(t,d) * idf(t, D) ( D: all documents, d: document, t: term ) https://en.wikipedia.org/wiki/Tf-idf
- 19. 19 Storing it in the graph Movie/ User Movie/ User { ML (Distance, Similarity, Embedding) } â Store ML outcomes on the edge â Enrich new/existing data and queries â Leverage benefits of ML â Reduce complexity
- 21. 21 â Personalized recommendation â Predictions based on combined external patterns â Depends on existing patterns being accurate â Can offer predictions with limited domain knowledge Collaborative Filtering
- 22. 22 Matrix Factorization âCan be efficient or not âSparse matrix âDimensionality Reduction âCombine with content-based âScale with faiss User 1 User 2 User 3 User 4 Toy Story 5 ? 2 1 Golden Eye ? 1 5 5 Love Actually ? 5 ? 5 Babe 5 ? 1 ? Star Trek 1 ? 5 5 SVD A = UÎŁV^T
- 23. 23 Matrix Factorization - Hybrid https://colab.research.google.com/github/arangodb/interactive_tutorials/blo b/master/notebooks/arangoflix/similarMovie_MF_ML_Inference.ipynb
- 25. 25 Graph Neural Networks Sachin Sharma ML Research Engineer @ArangoDB â Develop Intelligent Products â Former Machine Learning Scientist & Engineer @Define Media Gmbh â Former Research Intern @DFKI â AI Blogger â Interests: Graph ML, Vision, NLP. Graph ML, NVIDIA Triton, and ArangoDB: Thinking Beyond Euclidean Space https://www.arangodb.com/events/graphml-nvidia-triton-and-arangodb-thinking-beyond-euclidean- space/
- 26. 26 Graph(Node) Representation Learning image credits Stanford: â Map network nodes to d-dimensional embeddings space â Similar nodes in the network should remain close to each other in the embedding space Similarity of (u, v) in network Dot product between node embeddings
- 27. 27 Graph This is the key to machine learning on graphs, where each node is mapped into a coordinate system so certain properties are maintained. e.g., different node types can easily be separated by a line, or neighbouring nodes are close to each other. Embedding Embedding
- 28. 28 Can we Apply CNNs on Graphs? Fixed Number of Neighbors (2D Grid - Euclidean Space) Random Number of Neighbors (Graph - Non-Euclidean Space) image credits: source Image as 2D Grid Text/Audio as 1D Sequence
- 29. 29 Graph Neural Networks âNode classification âGraph classification âLink prediction â Predict links for users and movies
- 31. 31 ML + Graph Databases âKnowledge graph serves data âGraph naturally pairs with ML âML Ecosystem for graph interface Movie Knowledge Graph ML Ecosystem Embeddings/ Inferences Input data
- 32. 32 ArangoML - Ecosystem âNetworkX âDGL âCuGraph âArangoRDF âArangoML Pipeline âPyTorch Geometric â⊠more to come
- 33. 33 Nvidia Triton Meets ArangoDB AI Model Repository Deploy Graph ML Model (GraphSage) Front-End Client Application N3 N1 N2 N4 N5 N6 ArangoDb Update Update N3 N1 N2 N4 N5 N6 ArangoDb Retrieve all the node embeddings of the nbors of node âN5â which are at 1-Hop distance Know Surroundings
- 34. 34 Takeaway âGraph Databases âRecommendation Systems âML + Graph Databases âKeep Learning â> Recommender Systems Specialization Google ML Course Singular Value Decomposition (SVD) Steve Brunton YouTube | Website
- 35. 35 Thank you! âNotebooks https://github.com/arangodb/interactive_tutorials â Collaborative Filtering with AQL â Content-based Recommendations with ArangoSearch and TFIDF â Content-based Recommendations with FAISS, TFIDF, and Python â Graph Neural Networks with PyTorch â Matrix Factorization Test-drive ArangoDB and ArangoML using Oasis 14-days for free https://github.com/arangoml/ Register now at https://bit.ly/3blNaKR
Hinweis der Redaktion
- Compress matrix to something like this example. We have sparse data but can now attempt to offer predictions based on the reduced dimensions. Learn features such as genre and how much an item actually expresses that genre (is it sci-fi and action or sci-fi but more drama) Content based can match descriptions based on keyword and that isnât always enough.
- Must encode information about the graph (neighbors) for message passing - graph representational learning -
- Challenge of going from 2d fixed euclidean space - CNN require your data is represented in a fixed euclidean space. With non-euclidean there is variable number of neighbors.