Recsys

From Random IDs to Semantic IDs: Building a Generative Recommender from Scratch

How RQVAE compresses item embeddings into meaningful tokens, enabling TIGER-style generative recommendation. Full code walkthrough on Steam Games with…

9 hrs ago

From RNNs to Transformers: Building Sequential Recommenders (Part 1)

RecSys Series Part 9a: Implementing GRU4Rec and SASRec on Steam Games — with production deployment patterns

Apr 18

Your Ranking Model Is Right. Your Recommendations Are Wrong

RecSys Series Part 8: How diversity, freshness, and business constraints turn a ranked list into a product-ready feed

Apr 11

3 Modern Approaches to Solving Cold Start in RecSys

Contextual bandits, meta-learning, and LLMs — how Spotify, TikTok, and YouTube handle new users and items. The practitioner's guide to cold start.

Mar 25

From Candidates to Clicks: The Engineering Anatomy of Ranking

How modern recommendation systems go from 1,000 candidates to the one item you actually tap

Mar 14

Vector Search at Scale: The Production Engineer's Guide

IVF partitions space. PQ compresses memory. Together they make 100M vector search actually possible

Feb 3

How YouTube Finds Your Next Video in Milliseconds

A deep dive into two-tower retrieval, in-batch negatives, and the tricks that make it work

Jan 26

The 3-Stage Funnel Behind Every Modern Recommender System

Two-Tower models, vector databases, cross-encoders—and how they work together at scale

Jan 20

How Recommendation Systems Learned to Think

Recsys Series Part 2: From collaborative filtering breakthroughs to generative AI agents that can chat about your preferences and explain their…

Oct 4, 2025

RecSys Fundamentals: The Art and Science of Digital Matchmaking

Recsys Series Part 1: Master the three core approaches that power every modern recommendation engine

Sep 27, 2025

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