This article walks through the complete machine‑learning workflow for predicting user actions—covering core concepts, data collection, preprocessing, feature engineering, model training, evaluation, hyper‑parameter tuning, deployment, and future directions—using Python and popular AI libraries.
The article reviews recent deep‑learning breakthroughs in personalized content recommendation, covering news and e‑commerce systems, JD Digits' multi‑dimensional user behavior prediction models, knowledge‑graph meta‑learning, and the impact of multimodal AI on future recommendation technologies.
This article explains how sequence embedding can be applied to user browsing data on an automotive website to predict intended car models, addressing data sparsity and leveraging temporal information to improve prediction accuracy by 3%.
This article outlines six practical techniques—background execution, pre‑loading content, caching, UI‑first networking, predictive task start, and input minimization—to make mobile apps feel faster and more responsive, illustrated with real‑world examples from Instagram, Weibo, App Store, WeChat, and more.
