GraphAware Blog - Recommendations

Context-aware and hybrid recommendations

11 Aug 2022 by Alexandra Klacanova · 6 min read Beginner business Recommendations

So far, we have learned about collaborative filtering, content-based, and session-based recommendations. None of these approaches takes the situational context under consideration. Factors such as mood, occasion, location, company, etc., can affect user preferences and needs. Context-aware recommendations take these conditions into account to provide more relevant recommendations.

Session-based recommendations with Graphs

14 Jun 2022 by Alexandra Klacanova · 6 min read Beginner business Recommendations

Graph-Powered Machine Learning has already introduced us to content-based recommendations and collaborative filtering. These are the two most used approaches to providing recommendations. However, they both need information about the users to do so. What if you do not have user information? That’s where session-based recommendations come in.

Intro to collaborative filtering

12 May 2022 by Alexandra Klacanova · 7 min read Beginner business Recommendations

Welcome back to the Graph-Powered Machine Learning book club. Now we are in the section of the book that focuses on recommendations. In the last blog, I summed up how content-based recommendations work. In the fifth chapter, the author Alessandro Negro introduces us to collaborative filtering.

What are content-based recommendations?

19 Apr 2022 by Alexandra Klacanova · 7 min read Beginner business Recommendations

So far, the Graph-Powered Machine Learning book has introduced us to graphs and machine learning. The second part of the book talks about recommendations. Recommender systems (RS) gather information about users and items and provide item suggestions, bringing great value to online stores - clothing stores, bookstores, you name it. Companies like Netflix base their entire businesses on high performing recommender systems.

How does graph-based recommendation work

18 Aug 2021 by Alexandra Klacanova · 4 min read Hume Recommendations

Not so long ago, our very own Luanne gave an amazing talk titled Maltaware: Discovering what to drink with Neo4j on Nodes2020. Luanne demonstrated the value of graphs, and why they are the perfect fit for recommendation engines with an example of a whisky recommendation engine. Let me quickly walk you through a summary of why graphs and recommendation engines go together so well.

Relevant Search Leveraging Knowledge Graphs with Neo4j

05 May 2017 by Alessandro Negro · 14 min read Neo4j Elasticsearch Knowledge Graph Search NLP Recommendations

“Relevance is the practice of improving search results for users by satisfying their information needs in the context of a particular user experience, while balancing how ranking impacts business’s needs.” [1]

Bridging similarity islands in recommendation systems with Neo4j

08 Mar 2017 by Miro Marchi · 9 min read Neo4j Recommendations

Recommendation engines are a crucial element in the global trend towards a push-based web experience and away from a pull-based one. They provide the ability to personalize content offered to each user by predicting the interest the user will have in the recommended items. This is not only a powerful business tool for content providers, but also a vital improvement to the user experience. In today’s world where the volume, interdependence, variety and speed of information is overwhelming, recommendation engines can significantly reduce the gap between us and what we search for. Indeed, these engines are used even to enhance...

Graph-Aided Search - The Rise of Personalised Content

20 Apr 2016 by Alessandro Negro, Christophe Willemsen · 26 min read Neo4j Cypher Recommendations Elasticsearch

In our previous blog postwe introduced the concept of Graph Aided Search. It refers to a personalised user experience during search where theresults are customised for each user based on information gathered about them (likes, friends, clicks, buying history, etc.).This information is stored in a graph database and processed using machine learning and/or graph analysis algorithms.

Faster Recommendations with Neo4j 2.3 Triadic Selection

20 Oct 2015 by Alessandro Negro, Christophe Willemsen · 8 min read Neo4j Cypher Recommendations

Recently, Neo Technology announced the 2.3.0-RC1 release of their Neo4j graph database. One of the key new features is TriadicSelection built into Cypher’s Cost Based Planner. In this blog post, we will explore the Triadic Selection in detailand demonstrate how significantly it can speed up recommendations computed in Neo4j.

Recommendations with Neo4j and Graph-Aided Search

30 Sep 2015 by Michal Bachman · 4 min read Neo4j Recommendations Search Elasticsearch

For the last couple of years, Neo4j has been increasingly popular as the technology of choice for people building real-time recommendation engines. Having been at the forefront of the graph movement through clientengagements and open-source software development, we have identified the next step in the natural evolution of graph-based recommendationengines. We call it Graph-Aided Search.