Counters, Filters, and HashingI’ve already written about counters and how to remove bias from them, but I haven’t touched on some important technical aspects…Apr 9Apr 9
GPU-Powered Retrieval: Recent TrendNot too long ago, I learned about a new trend in our industry. It’s happening in an area where everything seemed to be working well…Oct 6, 2024Oct 6, 2024
The Ultimate Question of Diversity, Exploration, and Value in Recommender SystemsReformulating the challenges of ‘beyond accuracy’ aspects through the lens of value optimizationFeb 15, 2024Feb 15, 2024
Personalization and Popularity BiasIs popularity bias just a convenient excuse?Dec 26, 2023Dec 26, 2023
Published inTDS ArchiveThe Principled Approach to Early Ranking StagesA systematic method for designing and evaluating candidate generation and early ranking stages in recommender systems.Dec 6, 2023A response icon1Dec 6, 2023A response icon1
Beyond External Embeddings: Integrating User Histories for Enhanced RecommendationsSome time ago, I wrote about how to use external embeddings. Today, let’s discuss a related (even overlapping) topic: how to use external…Nov 29, 2023Nov 29, 2023
Published inTDS ArchiveTwo-Tower Networks and Negative Sampling in Recommender SystemsUnderstand the key elements that power advanced recommendation enginesNov 24, 2023A response icon4Nov 24, 2023A response icon4
Rating Systems: The Math Behind StarsMany services feature ratings of objects: products, movies, applications, organizations on a map. How should these be accurately…Nov 16, 2023Nov 16, 2023
Published inTDS ArchiveFrom Hacks to Harmony: Structuring Product Rules in RecommendationsDon’t let heuristics undermine your ML, learn to combine themSep 23, 2023Sep 23, 2023
