This article explains Huya Live's recommendation architecture, covering business background, system design, vector retrieval challenges and solutions with ScaNN, and the fine‑ranking pipeline, while highlighting performance optimizations, scalability, and future directions for their live‑streaming platform.
This article details how 58.com leverages deep learning models such as DNN, Wide&Deep, DeepFM, DIN and DIEN, combined with extensive user‑behavior feature engineering, offline vectorization, and online TensorFlow‑Serving pipelines to improve home‑page recommendation click‑through rates and overall platform efficiency.
This article explains how Alibaba's App recommendation pipeline integrates marketing scenario cards using weak personalization and machine‑learning models, detailing the metrics, feature engineering, recall and ranking strategies that together raise exposure revenue and click‑through performance.
This article offers a comprehensive, system‑engineer‑focused guide to Learning to Rank, covering fundamental machine‑learning concepts, evaluation metrics, training approaches, and a detailed online ranking architecture with feature, recall, and model governance, illustrated by real‑world examples from Meituan‑Dianping.
This article explains the online side of recommendation systems, covering data flow, traffic allocation, AB testing, the gateway, recall mechanisms, and real-time ranking, while also describing how offline-trained models are deployed as binary weight files for live serving.
The Youku Tudou data team overhauled its video recommendation system by moving ranking from offline to online, detailing architectural changes, advantages, challenges, feature handling, offline evaluation, and model weight fusion to improve scalability and user experience.
