https://sergiomorapardo.github.io/tags/recommendation/Recent content in Recommendation on Sergio A. Mora Pardo | AI/ML Technical LeadHugoen-USSun, 06 Sep 2026 00:00:00 -0500https://sergiomorapardo.github.io/writing/node2vec-recommendations/Sun, 06 Sep 2026 00:00:00 -0500https://sergiomorapardo.github.io/writing/node2vec-recommendations/<p>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.</p> <p>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.</p>https://sergiomorapardo.github.io/work/personalized-product-discovery/Mon, 01 Jan 0001 00:00:00 +0000https://sergiomorapardo.github.io/work/personalized-product-discovery/<h2 id="the-problem">The problem</h2> <p>Affiliate product discovery depended on a static, curated landing experience. That limited the number of products users could explore and left little room for relevance to adapt to each session.</p> <p>The product opportunity was not simply to train a ranking model. It was to redesign the discovery system and prove, under controlled conditions, that personalization created incremental value across the funnel.</p> <h2 id="my-role">My role</h2> <p>I led the ML design for ranking and discovery, partnering across product and engineering to move from a curated landing to a continuous personalized feed. My contribution covered problem framing, ranking signals, experiment design, technical review, and interpretation of the results.</p>https://sergiomorapardo.github.io/work/causal-ml-remarketing/Mon, 01 Jan 0001 00:00:00 +0000https://sergiomorapardo.github.io/work/causal-ml-remarketing/<h2 id="the-problem">The problem</h2> <p>High-volume remarketing can optimize clicks while still sending messages to users who would have purchased anyway. The product needed to decide <strong>who</strong> should receive a message, <strong>when</strong> it should arrive, and <strong>what</strong> to recommend while measuring true incremental impact rather than correlation.</p> <h2 id="my-role">My role</h2> <p>I built and led the ML remarketing engine for push and email across eight Latin American countries. The work combined causal modeling, personalization, serving architecture, and a sustained program of controlled experimentation.</p>