Multi-Model Content Recommendation: An Ensemble Architecture

This paper describes a multi-model recommendation ensemble developed for CNET that combines four fundamentally different recommendation paradigms — topic modeling (LDA), user-user collaborative filtering (ALS), item-item collaborative filtering, and locality-sensitive hashing (LSH-MinHash) — under a meta-orchestration layer. Each model contributes a distinct recommendation signal: semantic content similarity, user behavior similarity, item co-consumption patterns, and efficient set-based similarity. The paper documents the architecture, the individual model designs, the orchestration strategy, and discusses the ensemble approach in the context of modern recommendation systems.