https://www.newabdullah.com/tags/multi-task-learning/ Recent content in Multi-Task Learning on Abdullah Al Mamun Hugo en newabdullah 2025 Wed, 12 Mar 2025 00:00:00 +0000 https://www.newabdullah.com/posts/the-evaluation-of-recsys-part-3/ Wed, 12 Mar 2025 00:00:00 +0000 https://www.newabdullah.com/posts/the-evaluation-of-recsys-part-3/ <h1 id="the-evaluation-of-recommendation-systems---part-3">The Evaluation of Recommendation Systems - Part 3</h1> <h2 id="context-why-this-post-matters-who-its-for-and-what-youll-learn">Context: Why This Post Matters, Who It’s For, and What You’ll Learn</h2> <p>Welcome to Part 3 of our four-part series on evaluating recommendation systems (RecSys)! In the previous installments, we laid the groundwork: Part 1 introduced foundational techniques like collaborative filtering (CF) and Matrix Factorization (MF), which excelled at capturing user-item interactions but assumed linearity, missing complex patterns. Part 2 explored Factorization Machines (FM) and XGBoost, which tackled sparse data and non-linear ranking but fell short on higher-order interactions and sequential behaviors. By 2016, these limitations spurred a seismic shift toward deep neural networks (DNNs), which transformed RecSys by learning intricate feature interactions, automating feature engineering, and addressing diverse tasks like sequential recommendations and multi-task optimization. This post traces that evolution from 2016 to 2023, diving into Neural Collaborative Filtering (NCF), Wide &amp; Deep Learning, DeepFM, Deep Interest Network (DIN), Deep Learning Recommendation Model (DLRM), and Adaptive Task-to-Task Fusion (AdaTT). It’s tailored for data scientists, ML engineers, and tech professionals—particularly those designing large-scale RecSys in domains like e-commerce, streaming, and advertising—who need a deep, technical understanding of these advancements.</p>