Content Recommendation From Links Shared on Twitter Using Neo4j and Python by William Lyon.
From the post:
Overview
I’ve spent some time thinking about generating personalized recommendations for articles since I began working on an iOS reading companion for the Pinboard.in bookmarking service. One of the features I want to provide is a feed of recommended articles for my users based on articles they’ve saved and read. In this tutorial we will look at how to implement a similar feature: how to recommend articles for users based on articles they’ve shared on Twitter.
Tools
The main tools we will use are Python and Neo4j, a graph database. We will use Python for fetching the data from Twitter, extracting keywords from the articles shared and for inserting the data into Neo4j. To find recommendations we will use Cypher, the Neo4j query language.
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Very clear and complete!
Enjoy!