5 Ways to Innovate and Delight Users with Real-Time Recommendations

Discover five unique and effective ways to innovate with real-time recommendations by leveraging the flexibility of a Neo4j graph database.

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Blazing Fast Recommendations: 5-Minute Interview with Tim Hanssen

Check out this 5-minute interview with Tim Hanssen, Founder and CTO of Mediaconnect, who uses Neo4j to build a real-time recommendation engine.

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Graph Algorithms in Neo4j: Graph Algorithms in Practice

Graph analytics have value only if you have the skills to use them and if they can quickly deliver the insights you need. This blog provides a hands-on example using Neo4j on data from Yelp’s Annual Dataset challenge. Graph algorithms… Read more →

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Large-Scale Real-Time Recommendations with Neo4j

Editor’s Note: This presentation was given by Tim Hanssen at GraphConnect 2018 in New York City. Presentation Summary Prepr is a multi-channel engagement platform that streamlines content workflows and powers valuable audience interactions. They were using the MySQL relational database… Read more →

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This Week in Neo4j – Anti-Money Laundering Investigation, Replicating The GitHub GraphQL API, Getting Started with machine learning on graphs

Happy New Year everybody, and welcome to our first version of TWIN4j of 2019. I’d only just got used to writing the year as 2018! This is traditionally a quiet week, but we’ve still got some good stuff for you… Read more →

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How Real-Time Recommendations Increase Revenues, Optimize Margins and Delight Customers [Infographic]

“You may also like” sounds simple, but there’s a lot happening behind the scenes. Real-time recommendations work best when they take into account both the user’s needs (what is of interest to them) and your business strategy (items you need… Read more →

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Graph Algorithms in Neo4j: Use Cases for Graph Transactions & Analytics

Today’s most pressing data challenges center around connections, not just tabulating discrete data. Graph analytics accelerate breakthroughs across industries with more intelligent solutions. This blog series is designed to help you better leverage graph analytics so you can effectively innovate… Read more →

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Powering Recommendations with a Graph Database: A Rapid Retail Example

It’s one thing to say that Neo4j streamlines real-time recommendations; it’s another to show you the code so you can see for yourself. In this series, we discuss how real-time recommendations support a number of different use cases, from product… Read more →

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Powering Recommendations with a Graph Database: Proven Business Benefits [+ Case Studies]

Relevant, real-time recommendations drive revenue, but they are challenging to deliver. That’s because good recommendations require bringing together so much data, surfacing the relationships between all that data and delivering just the right suggestion in context and in the moment.… Read more →

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Powering Recommendations with a Graph Database: Connect Buyer and Product Data

Effective recommendations increase revenue and drive up average order value. But delivering highly relevant, real-time recommendations requires as much context as possible. Connecting the user to the perfect recommendation is an art. In this three-part series, we explore using recommendations… Read more →

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Retail & Neo4j: Personalized Promotion & Product Recommendations

Today’s retailers face a number of complex and emerging challenges. Thanks to lower overhead and higher volume, online behemoths like Amazon can deliver products faster and at a lower price, driving smaller retailers out of business. In order to compete,… Read more →

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This Week in Neo4j – 2 April 2017

Welcome to this week in Neo4j where we round up what’s been happening in the world of graph databases in the last 7 days. It’s a varied episode this week – we’ve got a worm’s neuronal wiring graph, bitcoin explorers,… Read more →

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Who Cares What Beyoncé Ate for Lunch?

Editor’s Note: This presentation was given by Alicia Powers at GraphConnect Europe in April 2016. Here’s a quick review of what she covered: The global obesity epidemic How to verify your data model The key components of a recommendation engine… Read more →

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From the Neo4j Community: February 2016

In the Neo4j community last month, love was in the air. That love expressed itself as more nodes than ever in our community content. From articles and podcasts to GraphGists and other projects, our global graph of community members keeps… Read more →

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Your Perfect Mix Tape for Original Art: Artfinder + Neo4j [Community Post]

[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j.] They say that that good artists copy, but great artists steal, right? At Artfinder, the global… Read more →

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The 5-Minute Interview: Scott David, World Economic Forum

This week’s 5-minute interview is with Scott David, the Director of Information Interaction at the World Economic Forum. I caught up with Scott David for a video interview at GraphConnect San Francisco. Q: Please tell us a little bit about… Read more →

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Neo4j Doc Manager: Polyglot Persistence for MongoDB & Neo4j

[Editor’s Note:] This Neo4j Lab has been deprecated. The code is available in our GitHub project, but is no longer actively maintained. When building scalable applications, developers have a myriad of technologies to choose from, especially when choosing a database… Read more →

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The Future of Recommendation Engines: Graph-Aided Search

Editor’s Note: GraphAware is a Silver sponsor of GraphConnect San Francisco. Register for GraphConnect to meet Michal and other sponsors in person. For the last couple of years, Neo4j has been increasingly popular as the technology of choice for people… Read more →

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Graph Databases in the Enterprise: Real-Time Recommendation Engines

Whether your enterprise operates in the retail, social, services or media sector, offering your users highly targeted, real-time recommendations is essential to maximizing customer value and staying competitive. Unlike other business data, recommendations must be inductive and contextual in order… Read more →

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Congratulations to Aurelius, Välkommen to DataStax!

Congratulations to Aurelius on the news of your recent acquisition! The Aurelius team has always been our companions in promoting the value of relationships in data, and I wish you all the best as you embark upon the journey of… Read more →

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