Algorithm Trend Updates

  1. Features

    Trend reports: six places where measurement tools diverge

    Trend reports: the six design choices that make measurement tools disagree, why the sample is the deep problem, and what to check before quoting a figure.

  2. Features

    How viral trends spread: the ratio, the S curve, thresholds

    Viral trends trends 2027: the reproduction ratio, why growth is bounded by an S curve, thresholds, and the two exposure mechanisms people confuse.

  3. Maintenance

    5 details of feature updates statistics people miss

    Five overlooked details explain why feature update statistics mislead, from missing change registers to staged rollouts and self-selected surveys.

  4. Maintenance

    Ranking signals comparison: evidence types

    Ranking signals comparison for social feeds: what correlation studies, before-and-after tests, and account-level controls can show.

  5. Rules

    How to run ranking signals research on your social posts

    Ranking signals research on your own social account: choosing a signal to test, building a split that cannot flatter you, and reading the result honestly.

  6. Reviews

    How a claim about ranking signals turns into folklore

    Ranking signals timeline of a claim, from one account's odd week to folklore: seven stages, why aggregation is the dangerous one, and how to date a claim.

  7. Guides

    A working brief on recommendation systems platforms

    Six recommendation systems platforms compared by item lifetime, trial cost, error cost and integration effort, and what each shape forces a publisher to do.

  8. Industry

    How to think about recommendation systems research

    Recommendation systems research and its five kinds of evidence: the gap between offline and live results, reading a claim, and auditing your own material.

  9. Rules

    Seven risks in recommendation systems and what they mean for a publisher

    Seven risks in recommendation systems, from inference and narrowing to fabricated behavior, and what each one means for a publisher as controls arrive.

  10. Industry

    How to tell whether sharing or recommendation carried a viral spike

    How sharing and recommendation engines move a viral item, how the hourly shape of views shows which one carried a spike, and what you can plan before publishing.

Start here

Latest from Algorithm Trend Updates