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 6Oct 6
The Ultimate Question of Diversity, Exploration, and Value in Recommender SystemsReformulating the challenges of ‘beyond accuracy’ aspects through the lens of value optimizationFeb 15Feb 15
Personalization and Popularity BiasIs popularity bias just a convenient excuse?Dec 26, 2023Dec 26, 2023
Published inTowards Data ScienceThe Principled Approach to Early Ranking StagesA systematic method for designing and evaluating candidate generation and early ranking stages in recommender systems.Dec 6, 20231Dec 6, 20231
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 inTowards Data ScienceTwo-Tower Networks and Negative Sampling in Recommender SystemsUnderstand the key elements that power advanced recommendation enginesNov 24, 20233Nov 24, 20233
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 inTowards Data ScienceFrom Hacks to Harmony: Structuring Product Rules in RecommendationsDon’t let heuristics undermine your ML, learn to combine themSep 23, 2023Sep 23, 2023
Leveraging External Embeddings in Recommender Systems: A Practical GuideIn recommender systems, you frequently encounter situations where you have access to ‘external embeddings.’ These are embeddings trained…Sep 10, 20231Sep 10, 20231
SLIM: A Fast and Interpretable Baseline for Recommender AlgorithmsContinuing from our post about linear models, today we’ll delve into a specific case — Sparse Linear Methods (SLIM). Here’s what sets this…Aug 31, 2023Aug 31, 2023
