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Machine Learning + Graph Databases for Better Recommendations V1 08/06/2022
The document discusses the integration of machine learning and graph databases for improving recommendation systems, particularly through the ArangoDB and its Arangoflix project. It covers various techniques including collaborative filtering, content-based recommendations, and the use of graph neural networks. It outlines the advantages of graph databases in handling complex relationships and offers practical applications across diverse domains such as product recommendations and customer analysis.
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Machine Learning + Graph Databases for Better Recommendations V1 08/06/2022
- 1. 1 Machine Learning + GraphDatabases for Better Recommendations
- 2. 2 Chris Woodward Developer Relations Engineer @ArangoDB ●Training ● Development ● Community ● ArangoML ● Twitter: @cw00dw0rd ● Slack: Chris.ArangoDB ● Email: chris@arangodb.com
- 3. 3 The Agenda ●ArangoFlix Project ●GraphDatabase ●Recommendations ●Machine Learning Techniques ○ Collaborative Filtering with AQL ○ Content-based Recommendations with ArangoSearch and TFIDF ○ Content-based Recommendations with FAISS, TFIDF, and Python ○ Matrix Factorization ○ Graph Neural Networks with PyTorch ●Graph Database + ML
- 4. 4 What is ArangoFlix? ●MachineLearning + Graph Databases ●ArangoDB Oasis ●ArangoFlix Website
- 5. 5 What and Whyof Graph Making Relationships a First Class Citizen ● ArangoDB turns the value of data relationships into actionable results ● 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 ●Collection ofnodes and edges ●Naturally describes relations in data ●Feasibly handles large joins/traversals ●Built-in graph algorithms (K paths, shortest path, etc) ●Use Cases: ○ Fraud Detection ○ Supply Chain Management ○ Recommendations ○ Customer 360 ○ Network Management ○ Risk Management
- 7. 7 ML + GraphDatabases 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 aRecommendation System?
- 9. 9 ●Provides predictions tobusiness/users ●Business Driven ●Data Quality ●Privacy/explainability Considerations ●Domain Specific ●Implementation Methods ○ Content-Based ○ Collaborative Filtering ○ Hybrid/Group/Other Recommendation System/Engine
- 10. 10 Recommendation System -Use Cases Domains ●Products ●Jobs ●Destinations ●People ●Entertainment ●Research ●Search Companies ●Amazon, Newegg, Instacart ●GlassDoor, Indeed ●AirBnB, Maps, Kayak ●Reddit, LinkedIn, Twitter ●Netflix, Xbox, AppleTV ●Healthcare, Citation ●Google, Bing, (Page Rank)
- 11. 11 … Customer LOB Applications DATA DATA LAKE Big Data Processing ETL Business Rules OPERATIONAL DATA KnowledgeGraph 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 SimplifiedRecommendation Flow
- 13. 13 ArangoFlix - DemoSite ArangoDB Cloud https://cloud.arangodb.com Examples > Install > Demo https://flix.arangodb.com
- 14. 14 Stack Recommendations ●ArangoSearch & AQL ●TFIDF- Content Based ○ Sklearn ●Matrix Factorization - Collaborative Filtering ○ Surprise/SVD ●GNN ○ PyTorch Geometric ○ sBert Backend ●ArangoDB Cloud ○ Driver: python-arango ●Foxx Microservices ○ GraphQL Endpoint Frontend ●VueJS / VueX ●Cytoscape ●PrimeVue
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- 16. 16 Content-based Filtering ● Verypersonalized recommendation ● Uses existing data to offer predictions ● Typically requires domain knowledge ● 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: Howoften 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
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- 19. 19 Storing it inthe graph Movie/ User Movie/ User { ML (Distance, Similarity, Embedding) } ● Store ML outcomes on the edge ● Enrich new/existing data and queries ● Leverage benefits of ML ● Reduce complexity
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- 21. 21 ● Personalized recommendation ●Predictions based on combined external patterns ● Depends on existing patterns being accurate ● Can offer predictions with limited domain knowledge Collaborative Filtering
- 22. 22 Matrix Factorization ●Can beefficient or not ●Sparse matrix ●Dimensionality Reduction ●Combine with content-based ●Scale 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 = UΣV^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
- 24.
- 25. 25 Graph Neural Networks SachinSharma ML Research Engineer @ArangoDB ● Develop Intelligent Products ● Former Machine Learning Scientist & Engineer @Define Media Gmbh ● Former Research Intern @DFKI ● AI Blogger ● 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 imagecredits Stanford: ● Map network nodes to d-dimensional embeddings space ● 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 thekey 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 ApplyCNNs 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 ●Nodeclassification ●Graph classification ●Link prediction ○ Predict links for users and movies
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- 31. 31 ML + GraphDatabases ●Knowledge graph serves data ●Graph naturally pairs with ML ●ML Ecosystem for graph interface Movie Knowledge Graph ML Ecosystem Embeddings/ Inferences Input data
- 32. 32 ArangoML - Ecosystem ●NetworkX ●DGL ●CuGraph ●ArangoRDF ●ArangoMLPipeline ●PyTorch Geometric ●… more to come
- 33. 33 Nvidia Triton MeetsArangoDB 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 ‘N5’ which are at 1-Hop distance Know Surroundings
- 34. 34 Takeaway ●Graph Databases ●Recommendation Systems ●ML +Graph Databases ●Keep Learning –> Recommender Systems Specialization Google ML Course Singular Value Decomposition (SVD) Steve Brunton YouTube | Website
- 35. 35 Thank you! ●Notebooks https://github.com/arangodb/interactive_tutorials ○ CollaborativeFiltering with AQL ○ Content-based Recommendations with ArangoSearch and TFIDF ○ Content-based Recommendations with FAISS, TFIDF, and Python ○ Graph Neural Networks with PyTorch ○ Matrix Factorization Test-drive ArangoDB and ArangoML using Oasis 14-days for free https://github.com/arangoml/ Register now at https://bit.ly/3blNaKR
Editor's Notes
- #23 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 isn’t always enough.
- #27 Must encode information about the graph (neighbors) for message passing - graph representational learning -
- #29 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.