Submit Search
Upload
Graph Gurus Episode 2: Building a Movie Recommendation Engine
•
1 like
•
253 views
TigerGraph
Follow
Building a Movie Recommendation Engine
Read less
Read more
Software
Report
Share
Report
Share
1 of 37
Download now
Download to read offline
Recommended
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1
TigerGraph
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...
Neo4j
Graph Gurus 15: Introducing TigerGraph 2.4
Graph Gurus 15: Introducing TigerGraph 2.4
TigerGraph
Graph Gurus Episode 6: Community Detection
Graph Gurus Episode 6: Community Detection
TigerGraph
Graph-Based Network Topology Analysis for Telecom Operators
Graph-Based Network Topology Analysis for Telecom Operators
Neo4j
The Gremlin Graph Traversal Language
The Gremlin Graph Traversal Language
Marko Rodriguez
Privacy-preserving Data Mining in Industry (WWW 2019 Tutorial)
Privacy-preserving Data Mining in Industry (WWW 2019 Tutorial)
Krishnaram Kenthapadi
https://www.slideshare.net/neo4j/a-fusion-of-machine-learning-and-graph-analy...
https://www.slideshare.net/neo4j/a-fusion-of-machine-learning-and-graph-analy...
Neo4j
Recommended
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1
Graph Gurus Episode 26: Using Graph Algorithms for Advanced Analytics Part 1
TigerGraph
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...
Optimizing the Supply Chain with Knowledge Graphs, IoT and Digital Twins_Moor...
Neo4j
Graph Gurus 15: Introducing TigerGraph 2.4
Graph Gurus 15: Introducing TigerGraph 2.4
TigerGraph
Graph Gurus Episode 6: Community Detection
Graph Gurus Episode 6: Community Detection
TigerGraph
Graph-Based Network Topology Analysis for Telecom Operators
Graph-Based Network Topology Analysis for Telecom Operators
Neo4j
The Gremlin Graph Traversal Language
The Gremlin Graph Traversal Language
Marko Rodriguez
Privacy-preserving Data Mining in Industry (WWW 2019 Tutorial)
Privacy-preserving Data Mining in Industry (WWW 2019 Tutorial)
Krishnaram Kenthapadi
https://www.slideshare.net/neo4j/a-fusion-of-machine-learning-and-graph-analy...
https://www.slideshare.net/neo4j/a-fusion-of-machine-learning-and-graph-analy...
Neo4j
Graph Gurus Episode 11: Accumulators for Complex Graph Analytics
Graph Gurus Episode 11: Accumulators for Complex Graph Analytics
TigerGraph
ETL Made Easy with Azure Data Factory and Azure Databricks
ETL Made Easy with Azure Data Factory and Azure Databricks
Databricks
Neptune, the Graph Database | AWS Floor28
Neptune, the Graph Database | AWS Floor28
Amazon Web Services
Game balancing
Game balancing
Sayed Ahmed
Graph Gurus 23: Best Practices To Model Your Data Using A Graph Database
Graph Gurus 23: Best Practices To Model Your Data Using A Graph Database
TigerGraph
Knowledge Graphs and Graph Data Science: More Context, Better Predictions (Ne...
Knowledge Graphs and Graph Data Science: More Context, Better Predictions (Ne...
Neo4j
Gremlin's Anatomy
Gremlin's Anatomy
Stephen Mallette
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Flink Forward
How Graph Algorithms Answer your Business Questions in Banking and Beyond
How Graph Algorithms Answer your Business Questions in Banking and Beyond
Neo4j
Optimizing Your Supply Chain with the Neo4j Graph
Optimizing Your Supply Chain with the Neo4j Graph
Neo4j
Neanex - Semantic Construction with Graphs
Neanex - Semantic Construction with Graphs
Neo4j
Transforming AI with Graphs: Real World Examples using Spark and Neo4j
Transforming AI with Graphs: Real World Examples using Spark and Neo4j
Databricks
Graph based real-time inventory and topology for network automation - webinar...
Graph based real-time inventory and topology for network automation - webinar...
Neo4j
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...
TigerGraph
Predicting Influence and Communities Using Graph Algorithms
Predicting Influence and Communities Using Graph Algorithms
Databricks
Leveraging Generative AI to Accelerate Graph Innovation for National Security...
Leveraging Generative AI to Accelerate Graph Innovation for National Security...
Neo4j
Deep Learning for Recommender Systems
Deep Learning for Recommender Systems
Yves Raimond
Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...
Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...
Neo4j
Introduction to Knowledge Graphs and Semantic AI
Introduction to Knowledge Graphs and Semantic AI
Semantic Web Company
Graphs for Genealogists
Graphs for Genealogists
Neo4j
Movie Recommendation System - MovieLens Dataset
Movie Recommendation System - MovieLens Dataset
Jagruti Joshi
Software Suite for Movie Market Analysis
Software Suite for Movie Market Analysis
dariospin93
More Related Content
What's hot
Graph Gurus Episode 11: Accumulators for Complex Graph Analytics
Graph Gurus Episode 11: Accumulators for Complex Graph Analytics
TigerGraph
ETL Made Easy with Azure Data Factory and Azure Databricks
ETL Made Easy with Azure Data Factory and Azure Databricks
Databricks
Neptune, the Graph Database | AWS Floor28
Neptune, the Graph Database | AWS Floor28
Amazon Web Services
Game balancing
Game balancing
Sayed Ahmed
Graph Gurus 23: Best Practices To Model Your Data Using A Graph Database
Graph Gurus 23: Best Practices To Model Your Data Using A Graph Database
TigerGraph
Knowledge Graphs and Graph Data Science: More Context, Better Predictions (Ne...
Knowledge Graphs and Graph Data Science: More Context, Better Predictions (Ne...
Neo4j
Gremlin's Anatomy
Gremlin's Anatomy
Stephen Mallette
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Flink Forward
How Graph Algorithms Answer your Business Questions in Banking and Beyond
How Graph Algorithms Answer your Business Questions in Banking and Beyond
Neo4j
Optimizing Your Supply Chain with the Neo4j Graph
Optimizing Your Supply Chain with the Neo4j Graph
Neo4j
Neanex - Semantic Construction with Graphs
Neanex - Semantic Construction with Graphs
Neo4j
Transforming AI with Graphs: Real World Examples using Spark and Neo4j
Transforming AI with Graphs: Real World Examples using Spark and Neo4j
Databricks
Graph based real-time inventory and topology for network automation - webinar...
Graph based real-time inventory and topology for network automation - webinar...
Neo4j
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...
TigerGraph
Predicting Influence and Communities Using Graph Algorithms
Predicting Influence and Communities Using Graph Algorithms
Databricks
Leveraging Generative AI to Accelerate Graph Innovation for National Security...
Leveraging Generative AI to Accelerate Graph Innovation for National Security...
Neo4j
Deep Learning for Recommender Systems
Deep Learning for Recommender Systems
Yves Raimond
Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...
Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...
Neo4j
Introduction to Knowledge Graphs and Semantic AI
Introduction to Knowledge Graphs and Semantic AI
Semantic Web Company
Graphs for Genealogists
Graphs for Genealogists
Neo4j
What's hot
(20)
Graph Gurus Episode 11: Accumulators for Complex Graph Analytics
Graph Gurus Episode 11: Accumulators for Complex Graph Analytics
ETL Made Easy with Azure Data Factory and Azure Databricks
ETL Made Easy with Azure Data Factory and Azure Databricks
Neptune, the Graph Database | AWS Floor28
Neptune, the Graph Database | AWS Floor28
Game balancing
Game balancing
Graph Gurus 23: Best Practices To Model Your Data Using A Graph Database
Graph Gurus 23: Best Practices To Model Your Data Using A Graph Database
Knowledge Graphs and Graph Data Science: More Context, Better Predictions (Ne...
Knowledge Graphs and Graph Data Science: More Context, Better Predictions (Ne...
Gremlin's Anatomy
Gremlin's Anatomy
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...
How Graph Algorithms Answer your Business Questions in Banking and Beyond
How Graph Algorithms Answer your Business Questions in Banking and Beyond
Optimizing Your Supply Chain with the Neo4j Graph
Optimizing Your Supply Chain with the Neo4j Graph
Neanex - Semantic Construction with Graphs
Neanex - Semantic Construction with Graphs
Transforming AI with Graphs: Real World Examples using Spark and Neo4j
Transforming AI with Graphs: Real World Examples using Spark and Neo4j
Graph based real-time inventory and topology for network automation - webinar...
Graph based real-time inventory and topology for network automation - webinar...
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...
Using Graph Algorithms For Advanced Analytics - Part 4 Similarity 30 graph al...
Predicting Influence and Communities Using Graph Algorithms
Predicting Influence and Communities Using Graph Algorithms
Leveraging Generative AI to Accelerate Graph Innovation for National Security...
Leveraging Generative AI to Accelerate Graph Innovation for National Security...
Deep Learning for Recommender Systems
Deep Learning for Recommender Systems
Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...
Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...
Introduction to Knowledge Graphs and Semantic AI
Introduction to Knowledge Graphs and Semantic AI
Graphs for Genealogists
Graphs for Genealogists
Similar to Graph Gurus Episode 2: Building a Movie Recommendation Engine
Movie Recommendation System - MovieLens Dataset
Movie Recommendation System - MovieLens Dataset
Jagruti Joshi
Software Suite for Movie Market Analysis
Software Suite for Movie Market Analysis
dariospin93
Regression Model for movies
Regression Model for movies
Mohit Rajput
Netflix recommendation systems
Netflix recommendation systems
Mina Tafreshi
Project Management IMDB
Project Management IMDB
haktansen
Gashte Police (Police Patrol)
Gashte Police (Police Patrol)
Alireza Ranjbar SHourabi
Recommendation Engine with In-Database Machine Learning
Recommendation Engine with In-Database Machine Learning
TigerGraph
[한국어] Safe Multi-Agent Reinforcement Learning for Autonomous Driving
[한국어] Safe Multi-Agent Reinforcement Learning for Autonomous Driving
Kiho Suh
Graph Gurus Episode 28: In-Database Machine Learning Solution for Real-Time R...
Graph Gurus Episode 28: In-Database Machine Learning Solution for Real-Time R...
TigerGraph
RecSys Challenge 2014, SemWexMFF group
RecSys Challenge 2014, SemWexMFF group
Ladislav Peska
Game monetization and promotion in Asia (with focus on Indonesia, Japan, Sout...
Game monetization and promotion in Asia (with focus on Indonesia, Japan, Sout...
GameCamp
The Future of Television - AWS Summit Sydney 2018
The Future of Television - AWS Summit Sydney 2018
Amazon Web Services
Stop Guessing, Start Knowing: Interpreting your Qualitative and Quantitative ...
Stop Guessing, Start Knowing: Interpreting your Qualitative and Quantitative ...
Hannah Flynn
Animation Opportunity 2009
Animation Opportunity 2009
Lance Young
Building Better Products: Mixing Qualitative & Quantitative Data with Storybo...
Building Better Products: Mixing Qualitative & Quantitative Data with Storybo...
Shelley Reece
Raccomender engines
Raccomender engines
Alessio Palma
Real-world News Recommender Systems
Real-world News Recommender Systems
kib_83
End-to-end machine learning project in Arabic
End-to-end machine learning project in Arabic
AMR koura
Mastering Multiplayer Stage3d and AIR game development for mobile devices
Mastering Multiplayer Stage3d and AIR game development for mobile devices
Jean-Philippe Doiron
R markup code to create Regression Model
R markup code to create Regression Model
Mohit Rajput
Similar to Graph Gurus Episode 2: Building a Movie Recommendation Engine
(20)
Movie Recommendation System - MovieLens Dataset
Movie Recommendation System - MovieLens Dataset
Software Suite for Movie Market Analysis
Software Suite for Movie Market Analysis
Regression Model for movies
Regression Model for movies
Netflix recommendation systems
Netflix recommendation systems
Project Management IMDB
Project Management IMDB
Gashte Police (Police Patrol)
Gashte Police (Police Patrol)
Recommendation Engine with In-Database Machine Learning
Recommendation Engine with In-Database Machine Learning
[한국어] Safe Multi-Agent Reinforcement Learning for Autonomous Driving
[한국어] Safe Multi-Agent Reinforcement Learning for Autonomous Driving
Graph Gurus Episode 28: In-Database Machine Learning Solution for Real-Time R...
Graph Gurus Episode 28: In-Database Machine Learning Solution for Real-Time R...
RecSys Challenge 2014, SemWexMFF group
RecSys Challenge 2014, SemWexMFF group
Game monetization and promotion in Asia (with focus on Indonesia, Japan, Sout...
Game monetization and promotion in Asia (with focus on Indonesia, Japan, Sout...
The Future of Television - AWS Summit Sydney 2018
The Future of Television - AWS Summit Sydney 2018
Stop Guessing, Start Knowing: Interpreting your Qualitative and Quantitative ...
Stop Guessing, Start Knowing: Interpreting your Qualitative and Quantitative ...
Animation Opportunity 2009
Animation Opportunity 2009
Building Better Products: Mixing Qualitative & Quantitative Data with Storybo...
Building Better Products: Mixing Qualitative & Quantitative Data with Storybo...
Raccomender engines
Raccomender engines
Real-world News Recommender Systems
Real-world News Recommender Systems
End-to-end machine learning project in Arabic
End-to-end machine learning project in Arabic
Mastering Multiplayer Stage3d and AIR game development for mobile devices
Mastering Multiplayer Stage3d and AIR game development for mobile devices
R markup code to create Regression Model
R markup code to create Regression Model
More from TigerGraph
MAXIMIZING THE VALUE OF SCIENTIFIC INFORMATION TO ACCELERATE INNOVATION
MAXIMIZING THE VALUE OF SCIENTIFIC INFORMATION TO ACCELERATE INNOVATION
TigerGraph
Better Together: How Graph database enables easy data integration with Spark ...
Better Together: How Graph database enables easy data integration with Spark ...
TigerGraph
Building an accurate understanding of consumers based on real-world signals
Building an accurate understanding of consumers based on real-world signals
TigerGraph
Care Intervention Assistant - Omaha Clinical Data Information System
Care Intervention Assistant - Omaha Clinical Data Information System
TigerGraph
Correspondent Banking Networks
Correspondent Banking Networks
TigerGraph
Delivering Large Scale Real-time Graph Analytics with Dell Infrastructure and...
Delivering Large Scale Real-time Graph Analytics with Dell Infrastructure and...
TigerGraph
Deploying an End-to-End TigerGraph Enterprise Architecture using Kafka, Maria...
Deploying an End-to-End TigerGraph Enterprise Architecture using Kafka, Maria...
TigerGraph
Fraud Detection and Compliance with Graph Learning
Fraud Detection and Compliance with Graph Learning
TigerGraph
Fraudulent credit card cash-out detection On Graphs
Fraudulent credit card cash-out detection On Graphs
TigerGraph
FROM DATAFRAMES TO GRAPH Data Science with pyTigerGraph
FROM DATAFRAMES TO GRAPH Data Science with pyTigerGraph
TigerGraph
Customer Experience Management
Customer Experience Management
TigerGraph
Graph+AI for Fin. Services
Graph+AI for Fin. Services
TigerGraph
Davraz - A graph visualization and exploration software.
Davraz - A graph visualization and exploration software.
TigerGraph
Plume - A Code Property Graph Extraction and Analysis Library
Plume - A Code Property Graph Extraction and Analysis Library
TigerGraph
TigerGraph.js
TigerGraph.js
TigerGraph
GRAPHS FOR THE FUTURE ENERGY SYSTEMS
GRAPHS FOR THE FUTURE ENERGY SYSTEMS
TigerGraph
Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...
Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...
TigerGraph
How to Build An AI Based Customer Data Platform: Learn the design patterns fo...
How to Build An AI Based Customer Data Platform: Learn the design patterns fo...
TigerGraph
Machine Learning Feature Design with TigerGraph 3.0 No-Code GUI
Machine Learning Feature Design with TigerGraph 3.0 No-Code GUI
TigerGraph
Supply Chain and Logistics Management with Graph & AI
Supply Chain and Logistics Management with Graph & AI
TigerGraph
More from TigerGraph
(20)
MAXIMIZING THE VALUE OF SCIENTIFIC INFORMATION TO ACCELERATE INNOVATION
MAXIMIZING THE VALUE OF SCIENTIFIC INFORMATION TO ACCELERATE INNOVATION
Better Together: How Graph database enables easy data integration with Spark ...
Better Together: How Graph database enables easy data integration with Spark ...
Building an accurate understanding of consumers based on real-world signals
Building an accurate understanding of consumers based on real-world signals
Care Intervention Assistant - Omaha Clinical Data Information System
Care Intervention Assistant - Omaha Clinical Data Information System
Correspondent Banking Networks
Correspondent Banking Networks
Delivering Large Scale Real-time Graph Analytics with Dell Infrastructure and...
Delivering Large Scale Real-time Graph Analytics with Dell Infrastructure and...
Deploying an End-to-End TigerGraph Enterprise Architecture using Kafka, Maria...
Deploying an End-to-End TigerGraph Enterprise Architecture using Kafka, Maria...
Fraud Detection and Compliance with Graph Learning
Fraud Detection and Compliance with Graph Learning
Fraudulent credit card cash-out detection On Graphs
Fraudulent credit card cash-out detection On Graphs
FROM DATAFRAMES TO GRAPH Data Science with pyTigerGraph
FROM DATAFRAMES TO GRAPH Data Science with pyTigerGraph
Customer Experience Management
Customer Experience Management
Graph+AI for Fin. Services
Graph+AI for Fin. Services
Davraz - A graph visualization and exploration software.
Davraz - A graph visualization and exploration software.
Plume - A Code Property Graph Extraction and Analysis Library
Plume - A Code Property Graph Extraction and Analysis Library
TigerGraph.js
TigerGraph.js
GRAPHS FOR THE FUTURE ENERGY SYSTEMS
GRAPHS FOR THE FUTURE ENERGY SYSTEMS
Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...
Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...
How to Build An AI Based Customer Data Platform: Learn the design patterns fo...
How to Build An AI Based Customer Data Platform: Learn the design patterns fo...
Machine Learning Feature Design with TigerGraph 3.0 No-Code GUI
Machine Learning Feature Design with TigerGraph 3.0 No-Code GUI
Supply Chain and Logistics Management with Graph & AI
Supply Chain and Logistics Management with Graph & AI
Recently uploaded
Introduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdf
FerryKemperman
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
stazi3110
Recruitment Management Software Benefits (Infographic)
Recruitment Management Software Benefits (Infographic)
Hr365.us smith
Folding Cheat Sheet #4 - fourth in a series
Folding Cheat Sheet #4 - fourth in a series
Philip Schwarz
EY_Graph Database Powered Sustainability
EY_Graph Database Powered Sustainability
Neo4j
Der Spagat zwischen BIAS und FAIRNESS (2024)
Der Spagat zwischen BIAS und FAIRNESS (2024)
OPEN KNOWLEDGE GmbH
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
umasea
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
soniya singh
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
jennyeacort
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio, Inc.
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
Christoph Pohl
How to Track Employee Performance A Comprehensive Guide.pdf
How to Track Employee Performance A Comprehensive Guide.pdf
Livetecs LLC
Unveiling the Future: Sylius 2.0 New Features
Unveiling the Future: Sylius 2.0 New Features
Łukasz Chruściel
Cloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStack
VICTOR MAESTRE RAMIREZ
Buds n Tech IT Solutions: Top-Notch Web Services in Noida
Buds n Tech IT Solutions: Top-Notch Web Services in Noida
bntitsolutionsrishis
What are the key points to focus on before starting to learn ETL Development....
What are the key points to focus on before starting to learn ETL Development....
kzayra69
MYjobs Presentation Django-based project
MYjobs Presentation Django-based project
AnoyGreter
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
Christina Lin
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
Matt Ray
Cloud Data Center Network Construction - IEEE
Cloud Data Center Network Construction - IEEE
VICTOR MAESTRE RAMIREZ
Recently uploaded
(20)
Introduction Computer Science - Software Design.pdf
Introduction Computer Science - Software Design.pdf
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Recruitment Management Software Benefits (Infographic)
Recruitment Management Software Benefits (Infographic)
Folding Cheat Sheet #4 - fourth in a series
Folding Cheat Sheet #4 - fourth in a series
EY_Graph Database Powered Sustainability
EY_Graph Database Powered Sustainability
Der Spagat zwischen BIAS und FAIRNESS (2024)
Der Spagat zwischen BIAS und FAIRNESS (2024)
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
Russian Call Girls in Karol Bagh Aasnvi ➡️ 8264348440 💋📞 Independent Escort S...
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
Call Us🔝>༒+91-9711147426⇛Call In girls karol bagh (Delhi)
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
How to Track Employee Performance A Comprehensive Guide.pdf
How to Track Employee Performance A Comprehensive Guide.pdf
Unveiling the Future: Sylius 2.0 New Features
Unveiling the Future: Sylius 2.0 New Features
Cloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStack
Buds n Tech IT Solutions: Top-Notch Web Services in Noida
Buds n Tech IT Solutions: Top-Notch Web Services in Noida
What are the key points to focus on before starting to learn ETL Development....
What are the key points to focus on before starting to learn ETL Development....
MYjobs Presentation Django-based project
MYjobs Presentation Django-based project
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
ODSC - Batch to Stream workshop - integration of Apache Spark, Cassandra, Pos...
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
Cloud Data Center Network Construction - IEEE
Cloud Data Center Network Construction - IEEE
Graph Gurus Episode 2: Building a Movie Recommendation Engine
1.
Graph Gurus Episode
2 Building the Next Generation Recommendation Engine with a Graph Database, using TigerGraph’s GraphStudio visual SDK
2.
© 2018 TigerGraph.
All Rights Reserved Welcome ● Attendees are muted but you can talk to us via Chat in Zoom ● We will have 10 min for Q&A at the end ● Send questions at any time using the Q&A tab in the Zoom menu ● The webinar will be recorded ● A link to the presentation and reproducible steps will be emailed 2 Developer Edition Download https://www.tigergraph.com/developer/
3.
© 2018 TigerGraph.
All Rights Reserved Today’s Moderator ● BS in Electrical Engineering and Computer Science from UC Berkeley ● MS in Electrical Engineering from Stanford University ● PhD in Computer Science from Kent State University focused on graph data mining ● 15+ years in tech industry 3 Victor Lee, Director of Product Management
4.
© 2018 TigerGraph.
All Rights Reserved Today’s Guru ● BS in Computer Science from Fudan University, China ● Master in Computer Science from Cornell University ● With TigerGraph for over 3 years ● Co-author of GSQL query language ● Leading the design and development of GraphStudio 4 Renchu Song, Engineering Manager
5.
© 2018 TigerGraph.
All Rights Reserved 5 Developer Edition Download https://www.tigergraph.com/developer/ Building a movie recommendation engine 1. Example data set 2. Defining movie recommendation graph schema 3. Defining data mapping 4. Loading data 5. Implementing movie recommendation algorithm 6. Executing query through RESTFul endpoint
6.
© 2018 TigerGraph.
All Rights Reserved 6 Developer Edition Download https://www.tigergraph.com/developer/ 1. Example Data Set - MovieLens 20M • Size: 841M (after decompression) • 20 million ratings applied to 27,000 movies by 138,000 real users. • Download: https://grouplens.org/datasets/movielens/20m/
7.
© 2018 TigerGraph.
All Rights Reserved 7 Data Format - CSV movieId,title,genres 1,Toy Story (1995),Adventure|Animation|Children|Comedy|Fantasy 2,Jumanji (1995),Adventure|Children|Fantasy 3,Grumpier Old Men (1995),Comedy|Romance 4,Waiting to Exhale (1995),Comedy|Drama|Romance 5,Father of the Bride Part II (1995),Comedy 6,Heat (1995),Action|Crime|Thriller 7,Sabrina (1995),Comedy|Romance 8,Tom and Huck (1995),Adventure|Children 9,Sudden Death (1995),Action 10,GoldenEye (1995),Action|Adventure|Thriller 11,"American President, The (1995)",Comedy|Drama|Romance ... userId,movieId,rating,timestamp 1,2,3.5,1112486027 1,29,3.5,1112484676 1,32,3.5,1112484819 1,47,3.5,1112484727 1,50,3.5,1112484580 1,112,3.5,1094785740 1,151,4.0,1094785734 1,223,4.0,1112485573 1,253,4.0,1112484940 1,260,4.0,1112484826 1,293,4.0,1112484703 ... ratings.csv (20000264 rows)movies.csv (27278 rows)
8.
© 2018 TigerGraph.
All Rights Reserved 8 2. Defining Movie Recommendation Graph Schema 2 vertex types: person, movie 1 edge type: rate
9.
© 2018 TigerGraph.
All Rights Reserved 9 3. Defining Data Mapping movies.csvratings.csv person(id) movie(id, title, genres) rate(rating, rated_at)
10.
© 2018 TigerGraph.
All Rights Reserved 10 4. Loading Data • Loading speed depends on machine configuration • Here it takes about 2 minutes.
11.
© 2018 TigerGraph.
All Rights Reserved 11 5. Implementing Movie Recommendation Algorithm Collaborative Filtering “People who liked items which you like also like THESE OTHER ITEMS.” I liked Wonder Woman And Lady Bird Group A also liked Wonder Woman and Lady Bird … and The Shape of Water.
12.
© 2018 TigerGraph.
All Rights Reserved 12 Input: A person p, two integer parameters k1 and k2 Algorithm steps: 1. Find all movies p has rated; 2. Find all persons rated same movies as p; 3. Based on the movie ratings, find the k1 persons that have most similar tastes with p; 4. Find all movies these k1 persons rated that p hasn’t rated yet; 5. Recommend the top k2 movies with highest average rating by the k1 persons; Output: At most k2 movies to be recommended to person p. 5. Implementing Movie Recommendation Algorithm
13.
© 2018 TigerGraph.
All Rights Reserved 13 Input: A person p, two integer parameters k1 and k2 Algorithm steps: 1. Find all movies p has rated; 2. Find all persons rated same movies as p; 3. Based on the movie ratings, find the k1 persons that have most similar tastes with p; 4. Find all movies these k1 persons rated that p hasn’t rated yet; 5. Recommend the top k2 movies with highest average rating by the k1 persons; Output: At most k2 movies to be recommended to person p. p …... rate rate rate PRatedMovies PSet 5. Implementing Movie Recommendation Algorithm
14.
© 2018 TigerGraph.
All Rights Reserved 14 Input: A person p, two integer parameters k1 and k2 Algorithm steps: 1. Find all movies p has rated; 2. Find all persons rated same movies as p; 3. Based on the movie ratings, find the k1 persons that have most similar tastes with p; 4. Find all movies these k1 persons rated that p hasn’t rated yet; 5. Recommend the top k2 movies with highest average rating by the k1 persons; Output: At most k2 movies to be recommended to person p. p …... rate rate rate PRatedMovies PSet …... PeopleRatedSameMovies rate rate rate rate 5. Implementing Movie Recommendation Algorithm
15.
© 2018 TigerGraph.
All Rights Reserved 15 Input: A person p, two integer parameters k1 and k2 Algorithm steps: 1. Find all movies p has rated; 2. Find all persons rated same movies as p; 3. Based on the movie ratings, find the k1 persons that have most similar tastes with p; 4. Find all movies these k1 persons rated that p hasn’t rated yet; 5. Recommend the top k2 movies with highest average rating by the k1 persons; Output: At most k2 movies to be recommended to person p. p …... rate rate rate PRatedMovies PSet …... PeopleRatedSameMovies rate rate rate rate 5. Implementing Movie Recommendation Algorithm
16.
© 2018 TigerGraph.
All Rights Reserved 16 Input: A person p, two integer parameters k1 and k2 Algorithm steps: 1. Find all movies p has rated; 2. Find all persons rated same movies as p; 3. Based on the movie ratings, find the k1 persons that have most similar tastes with p; 4. Find all movies these k1 persons rated that p hasn’t rated yet; 5. Recommend the top k2 movies with highest average rating by the k1 persons; Output: At most k2 movies to be recommended to person p. p …... rate rate rate PRatedMovies PSet …... PeopleRatedSameMovies rate rate rate rate …... rate rate rate rate 5. Implementing Movie Recommendation Algorithm
17.
© 2018 TigerGraph.
All Rights Reserved 17 Input: A person p, two integer parameters k1 and k2 Algorithm steps: 1. Find all movies p has rated; 2. Find all persons rated same movies as p; 3. Based on the movie ratings, find the k1 persons that have most similar tastes with p; 4. Find all movies these k1 persons rated that p hasn’t rated yet; 5. Recommend the top k2 movies with highest average rating by the k1 persons; Output: At most k2 movies to be recommended to person p. p …... rate rate rate PRatedMovies PSet …... PeopleRatedSameMovies rate rate rate rate …... rate rate rate rate RecommendedMovies 5. Implementing Movie Recommendation Algorithm
18.
© 2018 TigerGraph.
All Rights Reserved 18 GSQL Edge Block And Accumulator Learn more at GSQL webinar: https://www.youtube.com/watch?v=EslAkGAEbFs Person:s movie:mrate:r r1{ rating = 2.5 } r2 { rating = 4.5 } r3 { rating = 0.5 } r4 { rating = 5.0 } r6 { rating = 5.0 } r7 { rating = 4.0 } @avgRating = 0 AvgAccum<double> @avgRating; ... // People = { Tom, Emily, Dan } ... AvgMovieRating = SELECT m FROM People:s -(rate:r)-> movie:m WHERE r.rating >= 1.0 ACCUM m.@avgRating += r.rating POST-ACCUM m.@avgRating += 1 ORDER BY m.@avgRating DESC LIMIT 2; Tom Emily Dan @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 Dunkirk The Matrix Zootopia Star War r5 { rating = 4.0 }
19.
© 2018 TigerGraph.
All Rights Reserved 19 GSQL Edge Block And Accumulator Learn more at GSQL webinar: https://www.youtube.com/watch?v=EslAkGAEbFs Person:s movie:mrate:r r1{ rating = 2.5 } r2 { rating = 4.5 } r3 { rating = 0.5 } r4 { rating = 5.0 } r6 { rating = 5.0 } r7 { rating = 4.0 } @avgRating = 0 AvgAccum<double> @avgRating; ... // People = { Tom, Emily, Dan } ... AvgMovieRating = SELECT m FROM People:s -(rate:r)-> movie:m WHERE r.rating >= 1.0 ACCUM m.@avgRating += r.rating POST-ACCUM m.@avgRating += 1 ORDER BY m.@avgRating DESC LIMIT 2; Tom Emily Dan @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 Dunkirk The Matrix Zootopia Star War r5 { rating = 4.0 }
20.
© 2018 TigerGraph.
All Rights Reserved 20 GSQL Edge Block And Accumulator Learn more at GSQL webinar: https://www.youtube.com/watch?v=EslAkGAEbFs Person:s movie:mrate:r r1{ rating = 2.5 } r2 { rating = 4.5 } r3 { rating = 0.5 } r4 { rating = 5.0 } r6 { rating = 5.0 } r7 { rating = 4.0 } @avgRating = 0 AvgAccum<double> @avgRating; ... // People = { Tom, Emily, Dan } ... AvgMovieRating = SELECT m FROM People:s -(rate:r)-> movie:m WHERE r.rating >= 1.0 ACCUM m.@avgRating += r.rating POST-ACCUM m.@avgRating += 1 ORDER BY m.@avgRating DESC LIMIT 2; Tom Emily Dan @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 Dunkirk The Matrix Zootopia Star War r5 { rating = 4.0 }
21.
© 2018 TigerGraph.
All Rights Reserved 21 GSQL Edge Block And Accumulator Learn more at GSQL webinar: https://www.youtube.com/watch?v=EslAkGAEbFs Person:s movie:mrate:r r1{ rating = 2.5 } r2 { rating = 4.5 } r4 { rating = 5.0 } r6 { rating = 5.0 } r7 { rating = 4.0 } @avgRating = 0 AvgAccum<double> @avgRating; ... // People = { Tom, Emily, Dan } ... AvgMovieRating = SELECT m FROM People:s -(rate:r)-> movie:m WHERE r.rating >= 1.0 ACCUM m.@avgRating += r.rating POST-ACCUM m.@avgRating += 1 ORDER BY m.@avgRating DESC LIMIT 2; Tom Emily Dan @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 @avgRating = 0 Dunkirk The Matrix Zootopia Star War r5 { rating = 4.0 } r3 { rating = 0.5 }
22.
© 2018 TigerGraph.
All Rights Reserved 22 GSQL Edge Block And Accumulator Learn more at GSQL webinar: https://www.youtube.com/watch?v=EslAkGAEbFs Person:s movie:mrate:r r1{ rating = 2.5 } r2 { rating = 4.5 } r3 { rating = 0.5 } r4 { rating = 5.0 } r6 { rating = 5.0 } r7 { rating = 4.0 } @avgRating = 2.5 AvgAccum<double> @avgRating; ... // People = { Tom, Emily, Dan } ... AvgMovieRating = SELECT m FROM People:s -(rate:r)-> movie:m WHERE r.rating >= 1.0 ACCUM m.@avgRating += r.rating POST-ACCUM m.@avgRating += 1 ORDER BY m.@avgRating DESC LIMIT 2; Tom Emily Dan @avgRating = avg(4.5 + 5.0) = 4.75 @avgRating = 4.5 @avgRating = 4.0 @avgRating = 0 @avgRating = 0 @avgRating = 0 Dunkirk The Matrix Zootopia Star War r5 { rating = 4.0 }
23.
© 2018 TigerGraph.
All Rights Reserved 23 GSQL Edge Block And Accumulator Learn more at GSQL webinar: https://www.youtube.com/watch?v=EslAkGAEbFs Person:s movie:mrate:r r1{ rating = 2.5 } r2 { rating = 4.5 } r3 { rating = 0.5 } r4 { rating = 5.0 } r6 { rating = 5.0 } r7 { rating = 4.0 } AvgAccum<double> @avgRating; ... // People = { Tom, Emily, Dan } ... AvgMovieRating = SELECT m FROM People:s -(rate:r)-> movie:m WHERE r.rating >= 1.0 ACCUM m.@avgRating += r.rating POST-ACCUM m.@avgRating += 1 ORDER BY m.@avgRating DESC LIMIT 2; Tom Emily Dan @avgRating = 0 @avgRating = 0 @avgRating = 0 Dunkirk The Matrix Zootopia Star War r5 { rating = 4.0 } @avgRating = 2.5 + 1 = 3.5 @avgRating = 4.75 + 1 = 5.75 @avgRating = 4.5 + 1 = 5.5 @avgRating = 4.0 + 1 = 5.0
24.
© 2018 TigerGraph.
All Rights Reserved 24 GSQL Edge Block And Accumulator Learn more at GSQL webinar: https://www.youtube.com/watch?v=EslAkGAEbFs Person:s movie:mrate:r r1{ rating = 2.5 } r2 { rating = 4.5 } r3 { rating = 0.5 } r4 { rating = 5.0 } r6 { rating = 5.0 } r7 { rating = 4.0 } AvgAccum<double> @avgRating; ... // People = { Tom, Emily, Dan } ... AvgMovieRating = SELECT m FROM People:s -(rate:r)-> movie:m WHERE r.rating >= 1.0 ACCUM m.@avgRating += r.rating POST-ACCUM m.@avgRating += 1 ORDER BY m.@avgRating DESC LIMIT 2; Tom Emily Dan @avgRating = 0 @avgRating = 0 @avgRating = 0 Dunkirk The Matrix Zootopia Star War r5 { rating = 4.0 } @avgRating = 3.5 @avgRating = 5.75 @avgRating = 5.5 @avgRating = 5.0
25.
© 2018 TigerGraph.
All Rights Reserved 25 Step 1. Find all movies p has rated; p …... rate { rating = 3.5 } rate { rating = 5.0 } rate { rating = 3.0 } PRatedMovies PSet { @rated = true, @ratingByP = 3.5 } { @rated = true, @ratingByP = 5.0 } { @rated = true, @ratingByP = 3.0 } 5. Implementing Movie Recommendation Algorithm
26.
© 2018 TigerGraph.
All Rights Reserved 26 Step 2. Find all persons rated same movies as p …... …... PRatedMovies PeopleRatedSameMovies @ratingByP = 3.5 @ratingByP = 5.0 @ratingByP = 3.0 rate rate rate rate rate rate 5. Implementing Movie Recommendation Algorithm
27.
© 2018 TigerGraph.
All Rights Reserved 27 Step 3. Find the k1 persons that have most similar tastes with p. A = [3.5, 3.0] B = [0.5, 5.0] @lengthASqr = 3.5 * 3.5 + 3.0 * 3.0 = 21.25 @lengthBSqr = 0.5 * 0.5 + 5.0 * 5.0 = 25.25 @dotProductAB = 3.5 * 0.5 + 3.0 * 5.0 = 16.75 @cosineSimilarity = 16.75 / sqrt(21.25) / sqrt(25.25) = 0.72311 5. Implementing Movie Recommendation Algorithm
28.
© 2018 TigerGraph.
All Rights Reserved 28 Step 3. Find the k1 persons that have most similar tastes with p. …... PRatedMovies PeopleRatedSameMovies @ratingByP = 3.5 @ratingByP = 5.0 @ratingByP = 3.0 @cosineSimilarity = 1 @lengthASqr = 3.5 * 3.5 + 3.0 * 3.0 = 21.25 @lengthBSqr = 0.5 * 0.5 + 5.0 * 5.0 = 25.25 @dotProductAB = 3.5 * 0.5 + 3.0 * 5.0 = 16.75 @cosineSimilarity = 16.75 / sqrt(21.25) / sqrt(25.25) = 0.72311 @cosineSimilarity = 0.99753 …... rating = 3.5 rating = 5.0 rating = 0.5 rating = 5.0 rating = 5.0 rating = 3.5 5. Implementing Movie Recommendation Algorithm
29.
© 2018 TigerGraph.
All Rights Reserved 29 Step 3. Find the k1 persons that have most similar tastes with p. …... PRatedMovies PeopleRatedSameMovies @cosineSimilarity = 1 @cosineSimilarity = 0.99753 …... @cosineSimilarity = 0.72311 5. Implementing Movie Recommendation Algorithm
30.
© 2018 TigerGraph.
All Rights Reserved 30 Step 4. Find all movies these k1 persons rated that p hasn’t rated yet. PeopleRatedSameMovies …... …... rate RecommendedMovies rate rate rate rate @rated = false @rated = false @rated = false 5. Implementing Movie Recommendation Algorithm
31.
© 2018 TigerGraph.
All Rights Reserved 31 PeopleRatedSameMovies …... …... rating = 2.5 RecommendedMovies rating = 5.0 rating = 4.5 rating = 4.5 rating = 1.0 @recommendScore = 1.75 @recommendScore = 4.75 @recommendScore = 4.5 Step 5. Recommend the top k2 movies with highest average rating by k1 persons. 5. Implementing Movie Recommendation Algorithm
32.
© 2018 TigerGraph.
All Rights Reserved 32 Step 5. Recommend the top k2 movies with highest average rating by k1 persons. PeopleRatedSameMovies …... …... rating = 2.5 RecommendedMovies rating = 5.0 rating = 4.5 rating = 4.5 rating = 1.0 @recommendScore = 1.75 @recommendScore = 4.75 @recommendScore = 4.5 5. Implementing Movie Recommendation Algorithm
33.
© 2018 TigerGraph.
All Rights Reserved 33 curl -X GET 'http://<MACHINE-IP>:9000/query/MyGraph/RecommendMovies?p=238&k1=50&k2=20' | python -m json.tool 6. Executing query through RESTFul endpoint
34.
© 2018 TigerGraph.
All Rights Reserved 34 Other Recommendation Algorithms Another example: https://doc.tigergraph.com/GSQL-Demo-Examples.html ● Content-based ○ Considers the genres of the movies Other approaches and formulas ● Profile the users and focus on similar users ● Other scoring systems Production systems using TigerGraph - Wish.com delivering real-time cross-sell and up-sell recommendations to over 300 million users and driving billions in annual revenue Person Movie Genre
35.
Q&A Please send your
questions via the Q&A menu in Zoom 35
36.
© 2018 TigerGraph.
All Rights Reserved Episode 3: Sept 12 Detecting Fraud and Money Laundering in Real-Time with a Graph DB https://info.tigergraph.com/graph-gurus-3 36 REGISTER FOR MORE WEBINARS AT https://www.tigergraph.com/ webinars-and-events/
37.
© 2018 TigerGraph.
All Rights Reserved Additional Resources 37 Compare the Developer Edition and Enterprise Free Trial https://www.tigergraph.com/download/ Guru Scripts https://github.com/tigergraph/ecosys/tree/master/guru_scripts Join our Developer Forum https://groups.google.com/a/opengsql.org/forum/#!forum/gsql-users Take the Developer Survey https://www.tigergraph.com/developer-edition-feedback-survey/ @TigerGraphDB youtube.com/tigergraph facebook.com/TigerGraphDB linkedin.com/company/TigerGraph
Download now