The essence of platform algorithms is a **user value maximization** mechanism: platforms want users to stay longer, generate more interactions, and return more frequently — algorithms align with this commercial goal. Understanding this helps predict algorithmic logic: content that satisfies users (dwell time, likes, comments, shares, return visits) gets promoted; content that causes quick exits gets deprioritized. Different platforms show significant algorithmic implementation differences due to varying user behavior patterns.
## Major Platform Algorithm Mechanics
**TikTok recommendation algorithm**: centered on the “Interest Graph” rather than the Social Graph (whose posts you follow). Even new accounts (0 followers) can have quality content pushed to millions of users — TikTok’s biggest traffic democratization feature. Key signals: completion rate (most important metric) → like rate → comment rate → share rate. On publication, content enters a cold-start pool (~200-500 plays); if completion rate exceeds threshold, it advances to larger pools (thousands, tens of thousands, millions), progressively amplified. Therefore TikTok’s golden rule: **hook attention in the first 3 seconds** (foundation for high completion rates), guide engagement at the end (boost like/comment rates).
**YouTube recommendation algorithm**: centered on **Watch Time and Satisfaction** as core signals; prior to 2016, click-through rate was primary, then shifted to watch time (because high-CTR but low-quality “clickbait” damaged user experience). Two main traffic sources: search traffic (YouTube is the world’s second-largest search engine) and recommendation traffic (homepage recommendations + sidebar “Up Next”). SEO optimization: keyword placement in titles, descriptions, and tags affects search ranking; thumbnail click-through rate affects recommendation weight.
**WeChat Official Accounts**: does not primarily rely on algorithmic push (in principle) — based on subscription relationships (users receive posts only after actively following). Traffic logic: social virality (Moments sharing/group forwarding) is the main new-user acquisition method. Algorithm involvement: WeChat’s “Kan Yi Kan” feature (articles friends have liked) offers limited interest-based recommendations, but at far smaller scale than TikTok/YouTube.
## Content Strategy Principles in the Algorithm Era
**Create for content, not for algorithms**: algorithm rules update every 6-12 months; over-optimizing for current algorithm logic leads to traffic crashes after updates. Content strategy built on “users genuinely benefit” is more algorithm-change-resistant. **Multi-platform distribution, one primary platform**: adapt the same content for different platform formats (long video → short clips → graphic summary → podcast audio), expanding distribution reach. **Data feedback loop**: regularly analyze what high-engagement content has in common (topic, format, title structure) and iterate content strategy.
See [Xiaohongshu Content Strategy](https://sunqi.org/xiaohongshu-content-strategy-en/) and [YouTube Creator Academy](https://creatoracademy.youtube.com/).
