Understanding Collaborative Filtering, Matrix Factorization, and Spark ALS for Recommendation Systems

This article explains the fundamentals of recommendation systems, introduces collaborative filtering (both user‑based and item‑based), derives the matrix‑factorization model with ALS optimization, provides a complete Python implementation, and demonstrates how to apply Spark ALS in both demo and production environments.

ALSSparkcollaborative filtering

0 likes · 29 min read

Understanding Collaborative Filtering, Matrix Factorization, and Spark ALS for Recommendation Systems

Huajiao Technology

Aug 27, 2019 · Artificial Intelligence

Mastering Collaborative Filtering: From Traditional Similarity to Deep Neural Models

This article provides a comprehensive technical overview of collaborative filtering, covering traditional user‑ and item‑based similarity methods, matrix‑factorization approaches for implicit feedback, various loss functions, and a suite of deep neural network models such as GMF, MLP, NeuMF, DMF, and ConvMF, together with implementation details, evaluation metrics, and practical deployment considerations.

Deep LearningRecommendation SystemsSpark

0 likes · 29 min read

Mastering Collaborative Filtering: From Traditional Similarity to Deep Neural Models

DataFunTalk

Jul 1, 2019 · Artificial Intelligence

Data-Driven Foundations for Building Recommendation Systems

The article explains how data serves as a critical asset for recommendation systems, outlining the necessary steps from understanding business problems and data dimensions to collection, cleaning, integration, and analysis, while distinguishing explicit and implicit user feedback and emphasizing data quality, timeliness, and relevance.

Data QualityETLRecommendation Systems

0 likes · 11 min read

Data-Driven Foundations for Building Recommendation Systems

StarRing Big Data Open Lab

Oct 27, 2016 · Artificial Intelligence

Why Explicit vs Implicit Feedback Matters in Recommender Systems

This article explains the difference between explicit and implicit user feedback, discusses their advantages and pitfalls, and shows how collaborative‑filtering techniques such as user‑based, item‑based, adjusted cosine similarity, and Slope One can be applied to build accurate recommendation engines.

Slope Oneadjusted cosine similaritycollaborative filtering

0 likes · 19 min read

Why Explicit vs Implicit Feedback Matters in Recommender Systems

21CTO

Sep 20, 2016 · Artificial Intelligence

What Quora’s VP Reveals About Building Real‑World Recommender Systems

In this talk, Quora’s VP of Engineering Xavier Amatriain shares practical lessons from building the company’s large‑scale recommender system, covering data richness, implicit signals, model choices, feature engineering, evaluation strategies, and why distribution isn’t always required.

Model Evaluationfeature engineeringimplicit feedback

0 likes · 4 min read

What Quora’s VP Reveals About Building Real‑World Recommender Systems