This tutorial demonstrates how to use Python and Selenium to collect thousands of hot comments from QQ Music, parse the data from dynamically loaded list items, store it in CSV files, and finally generate a word‑cloud visualization of the comment content.
This tutorial walks through building a Python script that fetches a QQ Music QR code, displays it locally, monitors its status, extracts required encrypted parameters from cookies, and completes the login process by repeatedly polling the authentication endpoint to obtain a session.
The article explains that QQ Music desktop client can unexpectedly upload large amounts of data by acting as a PCDN node, but users can easily stop this behavior by disabling the playback acceleration service in the client settings, preserving their upstream bandwidth on both Windows and Mac.
This article presents a comprehensive overview of QQ Music's recall pipeline, detailing business characteristics, challenges such as noisy user behavior and cold‑start, and four major solutions—including knowledge‑graph‑enhanced recall, sequence‑based and multi‑interest modeling, audio‑based recall, and federated learning—along with practical insights and Q&A.
This talk presents the evolution of QQ Music's ranking system, detailing background challenges, user‑perception modeling, multi‑objective and causal learning to mitigate the Matthew effect, long‑tail content support, cross‑domain recommendation, and module personalization for diversified traffic, concluding with future research directions.
This tutorial walks through creating a Python‑based local QR‑code login scraper for QQ Music, covering the extraction of dynamic parameters, handling of encrypted cookies, displaying and removing QR images, and ultimately obtaining a usable session for further automation.
This talk details the evolution of QQ Music's ranking system, covering background, user‑perception modeling, pairwise optimization, advanced model architectures, multi‑objective learning with causal inference to mitigate the Matthew effect, cross‑domain recommendation, and module personalization that together boost user engagement and platform traffic.
This article explains why video integration is essential for modern music products, introduces QQ Music's PortrAIt AI vertical video clipping technology, details its technical capabilities and business scenarios such as background videos and video playlists, and outlines current results and future development plans.
This article presents QQ Music’s end‑to‑end solution for data‑driven content understanding, value evaluation, and fine‑grained operation, detailing offline and real‑time pipelines, neural‑network models, a content middle‑platform, parameter services, and a precise delivery system that boost user engagement while preserving experience.
This tutorial walks you through using Python Selenium to automate QQ Music, scroll through the infinite‑scroll comment section, extract user avatars, names, timestamps and comment text, save the data to CSV, and finally visualize the comments with a word‑cloud image.
This tutorial walks through using Python's Selenium to automate scrolling and extract QQ Music hot comment data—including user avatars, names, timestamps, and content—then saves the information to CSV and creates a Chinese word cloud for visual analysis.
QQ Music replaced its slow Hive warehouse with a massive ClickHouse cluster, achieving sub‑second to ten‑second query latency on petabyte‑scale data, enabling real‑time analytics for non‑technical users, and following five operational best practices—ZooKeeper planning, idempotent writes, sensible partitions, read‑write separation, and localized joins—while leveraging Tencent Cloud’s managed ClickHouse service.
This tutorial walks through using Python to fetch QQ Music song rankings, lyrics, and hot comments, then iterates through comment pages to collect data and generate a word‑cloud visualization, illustrating the required libraries, code snippets, and key parameters.
This guide walks you through using Python's requests library to scrape QQ Music, showing how to retrieve a song’s ID, fetch its lyrics, and extract the top comments, complete with code snippets and step‑by‑step explanations.
Learn to retrieve song titles, album names, and playback links from QQ Music’s artist ranking pages using Python’s requests library, parse JSON responses from XHR calls, and store results in Excel, with a complete walkthrough from setup to final summary.
Android M introduced a dynamic runtime permission model that separates normal and dangerous permissions, requiring apps like QQ Music to implement runtime checks, adapt startup and feature‑triggered requests, use a permission‑guard shell, and handle OEM‑specific fragmentation issues such as sensor, shortcut, and floating‑window permissions.
