The article examines how Chinese regulations curbed forced app downloads, yet Zhihu still blocked full content for non‑logged‑in users, sparking user backlash, speculation about AI data protection, and ultimately leading the platform to lift the login barrier.
This tutorial explains how to use Python 3 with Requests, lxml, and Baidu's AipFace SDK to crawl images from Zhihu's "美女" topic, filter them by face detection, gender, authenticity, and beauty score, and store the qualified pictures locally.
This article presents a method for massive multi-label text classification on Zhihu content by combining a semantic retrieval model with a proprietary large AI model, detailing the challenges of large label spaces, model architecture, loss optimization, and experimental results showing significant accuracy gains.
This article presents a comprehensive overview of Zhihu's Bridge platform, detailing its content‑operation architecture—including content pool, management, analysis, monitoring, and intervention modules—explaining the underlying streaming and batch technologies such as Flink, Doris, and Elasticsearch, and outlining future automation and AI‑driven workflow directions.
This article explains how to collect images from Zhihu's "beauty" topic using Python's Requests and lxml libraries, filter them with Baidu AI's AipFace face detection service, and store the qualified pictures locally, detailing the required environment, logic, and preparation steps.
This article details Zhihu's Data Management Platform (DMP), covering the business problems it solves, the end‑to‑end workflow, feature taxonomy, system architecture, data pipelines for batch and streaming, audience targeting processes, performance challenges, and future technical directions.
This article demonstrates how to automate Zhihu login on Windows by analyzing network traffic with Fiddler, extracting required parameters, and implementing a Python script that builds HTTP requests using urllib2, handles cookies, captcha retrieval, and logs the results, complete with sample code and execution screenshots.
This tutorial demonstrates how to build a Scrapy spider that crawls Zhihu user follow and follower data via Zhihu’s public APIs, handles request headers, parses JSON responses, paginates results, and stores the extracted information into MongoDB using a custom item pipeline.
This article examines Zhihu’s evolution from a text‑centric platform to a video‑enabled product, detailing the design team’s role in content flow, the creation of video‑answer and video‑entity formats, the development of rapid‑layout tools, the shift to a membership model, and the ethical considerations designers must balance between user experience and commercial goals.
This article presents a comprehensive overview of Zhihu's recommendation page ranking system, detailing the request flow, ranking evolution from time‑based to deep‑learning models, feature engineering strategies, model architectures such as DNN, DeepFM, DIN, multi‑task learning, and lessons learned for production deployment.
This article details Zhihu's read‑service architecture, covering its business requirements, high‑availability and high‑performance design goals, key components such as Proxy, Cache and Storage, extensive performance metrics, the migration from MySQL to TiDB, and the benefits brought by TiDB 3.0 features.
This tutorial demonstrates how to use Python's requests library to simulate Zhihu login by handling dynamic _xsrf tokens, optional captcha verification, saving cookies, and then crawling the main page to extract questions and answer abstracts.
This article walks through the complete process of designing a Python web scraper to collect Zhihu answer data, parse author, ID and excerpt fields, store the results in CSV files, and finally visualize the text with a word‑cloud, including all necessary code snippets and explanations.
Using Python to scrape 4,277 Zhihu answers about changing WeChat IDs, this article shows how the data was collected, presents the extraction code, and uncovers six surprising patterns in users' chosen usernames, illustrating social trends behind the recent feature update.
This article details how Zhihu’s Moneta service tackled massive data growth—over a trillion rows—by migrating from MySQL sharding to the HTAP‑capable TiDB platform, describing architectural changes, performance gains, migration lessons, and future expectations with TiDB 3.0.
This guide walks through using Python's requests and BeautifulSoup libraries to simulate Zhihu login, extract user profiles, retrieve answer likers, download avatars, fetch all answers for a question, and store the collected data in a SQLite database.
This article details Zhihu's real‑time data warehouse evolution, describing the 1.0 Spark Streaming architecture, its limitations, and the 2.0 redesign that introduces Flink, layered data models, streaming and batch ETL, metric storage choices, and future roadmap for scalable, low‑latency analytics.
This guide shows how to use Selenium WebDriver in Java to bypass Zhihu's captcha by logging in through a third‑party account, handling multiple windows and an iframe, and then returning to the original page.
This article explores Zhihu’s product design workflow, from requirement gathering and solution planning through design output and effect evaluation, highlighting the role of intuition, visual communication, metric-driven assessment, and how design uniquely adds business value and bridges user experience with product strategy.
Zhihu’s Morph dynamic solution, built on a Flexbox‑based DSL and JSON, enabled rapid, cross‑platform updates of mobile ad cards, dramatically reducing rollout time from eight days to one and supporting over 70 styles with minimal impact on app size and performance.
In a playful data‑driven experiment, the author scraped 27,664 Zhihu answers to “What are your dating criteria?”, filtered out short, outdated, high‑profile or already‑matched posts, applied follower‑and engagement‑thresholds to narrow the pool to 480 candidates, then ranked the top 30 by a like‑to‑comment ratio, sharing the code and dataset for reproducibility.
This article walks through a complete data‑analysis workflow—scraping Zhihu dating‑preference answers, cleaning and filtering the data, deriving gender and activity metrics, designing a four‑step screening process, and finally ranking candidates with a custom like‑to‑comment index—to help a single programmer create a concise, high‑quality list of potential partners.
This article walks through creating a Python‑based Zhihu crawler, visualizing the harvested user data with the ELK stack, and offers practical improvements such as threading, Redis URL storage, and MongoDB persistence for more efficient and scalable web scraping.
This article shows how to use Python to scrape Zhihu answers, store them in MongoDB, filter for short high‑upvote replies, and then presents a collection of programmer‑centric jokes that illustrate the kind of "god replies" the crawler can retrieve.
This article walks through using Python's requests and BeautifulSoup libraries to simulate Zhihu login, extract user profiles, retrieve answer likers, download avatars, collect all answers for a question, and store the data in SQLite, while highlighting common challenges and future enhancements.
This article walks through creating a Python web crawler for Zhihu, covering simulated login, fetching user profiles, extracting answer likers, downloading avatars, gathering all answers for a question, and storing the collected data in a SQLite database.
In an interview, Zhihu senior vice‑president Li Dahai explains how AI, big data and Tencent Cloud enable Zhihu’s knowledge‑matching algorithms, boost answer volume by 300%, and shape the platform’s vision of connecting expert users while emphasizing principles over pure algorithmic tricks.
This tutorial explains how a Python developer can set up a Scrapy project, write spiders to crawl Zhihu user profiles, store the results in a MySQL database, adjust settings for headers and delays, and finally perform simple gender and location analysis on the collected data.
Analyzing over 3.2 million publicly available Zhihu profiles collected via a distributed Python crawler, this report uncovers gender balance near 1:1, top residential cities, dominant occupations, university participation, and the most followed and active contributors, while noting data limitations and temporal relevance.
This article walks through creating a Python-based Zhihu web crawler, detailing the tech stack, data collection, visualization of user demographics and top contributors, the crawler architecture, authorization handling, and suggestions for performance and storage improvements.
This article walks through building a Python web scraper for Zhihu, covering login simulation, extracting user profiles, answer likers, followers, avatars, and all answers of a question, and storing the collected data in SQLite, while highlighting challenges like captcha and anti‑scraping limits.
This guide shows how to collect every @mentioned user on a Zhihu question page by using a JavaScript bookmarklet or a Python script, explains the extraction process, provides the necessary code snippets, and discusses why following programmers on Zhihu may not be the most effective learning method.
