Taylor Scott Amarel

Experienced developer and technologist with over a decade of expertise in diverse technical roles. Skilled in data engineering, analytics, automation, data integration, and machine learning to drive innovative solutions.

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Posts related to Recommendation Systems

Optimizing Embedding Tables with Vector Quantization: A Practical Guide

Introduction: The Embedding Bottleneck and the Promise of Vector Quantization In the ever-evolving landscape of machine learning, the size and speed of models are paramount. Embedding tables, which map discrete data like words or user IDs to dense vector representations, are often a significant bottleneck, consuming vast amounts of memory and slowing down inference. Imagine
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Revolutionizing Recommendations: A Deep Dive into Graph Neural Networks

The Rise of GNNs in Recommendation: A New Era of Personalization The relentless pursuit of personalized experiences has propelled recommendation systems to the forefront of technological innovation. From suggesting the next must-watch show on streaming services to curating tailored product lists on e-commerce platforms, these systems shape our digital interactions daily. While traditional methods like
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NCF vs. MF: A Deep Dive into Recommendation Algorithms

Introduction: The Rise of Personalized Recommendations In the realm of personalized experiences, recommendation systems have become indispensable. From suggesting the next binge-worthy series on streaming platforms to curating product recommendations on e-commerce sites, these systems shape our digital interactions. Two prominent techniques that have powered recommendation engines over the past decade (2010-2019) are Matrix Factorization
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