<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Node2Vec on Sergio A. Mora Pardo | AI/ML Technical Lead</title><link>https://sergiomorapardo.github.io/tags/node2vec/</link><description>Recent content in Node2Vec on Sergio A. Mora Pardo | AI/ML Technical Lead</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Sun, 06 Sep 2026 00:00:00 -0500</lastBuildDate><atom:link href="https://sergiomorapardo.github.io/tags/node2vec/index.xml" rel="self" type="application/rss+xml"/><item><title>From co-likes to candidates with Node2Vec</title><link>https://sergiomorapardo.github.io/writing/node2vec-recommendations/</link><pubDate>Sun, 06 Sep 2026 00:00:00 -0500</pubDate><guid>https://sergiomorapardo.github.io/writing/node2vec-recommendations/</guid><description>&lt;p&gt;A catalog can become a graph before it becomes a recommender. In the source notebook, two movies are connected when the same users rate both of them highly. Node2Vec then turns the resulting neighborhoods into vectors, so nearby movies can be retrieved with a cosine-similarity lookup.&lt;/p&gt;&#10;&lt;p&gt;That is a useful candidate-generation pattern. It is not, by itself, a complete recommendation system. This note separates what the notebook demonstrates from the evaluation and serving work that a production system would still need.&lt;/p&gt;</description></item></channel></rss>