Recommender Systems · Item-Item Models · Conceptual Analysis
SLIM · GL-SLIM · EASE — Conceptual Comparison Recommender Systems · Item-Item Models · Conceptual Analysis SLIM, GL-SLIM & EASE A conceptual comparison of three item-item collaborative filtering models — how they think about the same problem differently Abstract All three models answer the same question: given a user's interaction history, which items should we recommend? They all do it by learning an item-item weight matrix W such that a user's predicted preference vector is X·W . Yet they arrive at radically different solutions — one iterates with gradient descent over thousands of steps, one solves a single linear system in seconds, and one sits between both worlds by adding group-aware local models on top. Understanding why they differ is more useful than memorising their equations. §1 The Shared Foundation Every model in this family makes the same fundamental assumption:...