filmov
tv
Building a Real-time Recommendation Engine With Neo4j - Part 1/4 - William Lyon - OSCON 2017
Показать описание
All data and code available here:
Description
William Lyon demonstrates how to build a recommendation engine using Neo4j and Python. The solution will be a hybrid that makes use of both content-based and collaborative filtering to come up with multilayered recommendations.
William walks you through building the solution from scratch, explaining the decisions made along the way and sharing the factors that might lead to better recommendations for the end user. You’l learn how to model the data as a graph, explore data import with Neo4j, and use the Cypher query language to write real-time recommendation queries. You’ll also make use of Python data science tools to leverage graph algorithms and natural language processing techniques to enhance your recommender system.
What you'll learn
Explore the property graph data model
Discover how to import and query data using Cypher, the query language for graphs
Learn how to create a system capable of generating real-time personalized recommendations based on user data and how to use data science tools to enhance the model used for recommendations
Description
William Lyon demonstrates how to build a recommendation engine using Neo4j and Python. The solution will be a hybrid that makes use of both content-based and collaborative filtering to come up with multilayered recommendations.
William walks you through building the solution from scratch, explaining the decisions made along the way and sharing the factors that might lead to better recommendations for the end user. You’l learn how to model the data as a graph, explore data import with Neo4j, and use the Cypher query language to write real-time recommendation queries. You’ll also make use of Python data science tools to leverage graph algorithms and natural language processing techniques to enhance your recommender system.
What you'll learn
Explore the property graph data model
Discover how to import and query data using Cypher, the query language for graphs
Learn how to create a system capable of generating real-time personalized recommendations based on user data and how to use data science tools to enhance the model used for recommendations
Recommendation Engine Design Deep Dive with Google SWE! | Systems Design Interview Question 20
Building a Complete End-to-End Batch and Real-time Recommendation Engine by Chris Fregly
![[Free Workshop] Using](https://i.ytimg.com/vi/NkruES_9fJ0/hqdefault.jpg)
[Free Workshop] Using OpenAI to Build a Real-Time Recommendation Engine
Spotify ML Question - Design a Recommendation System (Full mock interview)
Building a real world recommendation engine (Michael Hunger)
Building a Real-Time Solr-Powered Recommendation Engine
Building a Real-time Recommendation Engine With Neo4j - Part 3/4 - William Lyon - OSCON 2017
Building a Recommendation Engine with Machine Learning Techniques (Brian Sam-Bodden) - FSF 2016
Christopher Yang, Co-President of Shopline, on leading the e-commerce backbone of stores and more!
Design TikTok's Recommendation System | ML System Design | #systemdesign
Building a Recommendation Engine Using Diverse Features
Real-Time Search and Recommendation at Scale Using Embeddings and Hopsworks
Building a Real-time Recommendation Engine With Neo4j - Part 4/4 - William Lyon - OSCON 2017
Luke Youngblood - Building a Serverless Recommendation Engine
System Design for Recommendations and Search // Eugene Yan // MLOps Meetup #78
Building a Real-time Recommendation Engine With Neo4j - Part 1/4 - William Lyon - OSCON 2017
How to Build a Recommendation Engine
Building a Large Scale Recommendation Engine with Spark and Redis ML - Shay Nativ
Case Study: Building a Production Recommendation Engine on Accumulo
How to build a real-time product recommendation engine with AWS and mParticle
Building a Real-time Recommendation Engine With Neo4j - Part 2/4 - William Lyon - OSCON 2017
Real Time Fitness Recommendation Engine - Custom Recommendations
Building Better Recommendation Engines
Run a Real-Time Product Recommendation Pipeline Engine with Spark
Комментарии