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Longer commitments

Series and courses

Playlists and videos that are one part of a longer run.

  1. Official source

    PDFs in Search: Indexing, AI & Pitfalls

    Google Search Central

    PDF files in search get their own treatment at Google, and Martin Splitt and John Mueller explain how they are indexed, when HTML is the better choice, and traps like rasterised text.

  2. Official source

    Do sitemaps still matter?

    Google Search Central

    Do XML sitemaps still matter? Martin Splitt and John Mueller look back at their history, ask whether crawlers can work out site structure alone, and discuss why fetch warnings in Search Console worry owners.

  3. Official source

    How to read the Indexing Report

    Google Search Central

    Should you worry when Search Console lists pages as not indexed? Martin Splitt and John Mueller explain how to read the Page Indexing report and whether a 404 always means a broken site.

  4. Trade press or conference

    Navigating website migrations - Emma Russell

    SearchNorwichXL

    Domain migrations are the focus of a SearchNorwich talk by Emma Russell, who explains the different kinds of migration, recounts a few that went badly, and offers an SEO framework.

  5. Sells in this field

    Google SEO Ranking Factor Tier List (Part 2)

    Matt Diggity

    How do guest posts, PBNs and press releases rank as link building techniques? Matt Diggity sorts 21 of them into a tier list, in part two of a ranking factor series.

  6. Sells in this field

    Elastic Snackable Series: How to evaluate hybrid search

    Elastic

    Hybrid search mixes keyword matching with vector-based retrieval, and this short Elastic guide explains how to judge it, including reciprocal rank fusion scoring and the unified API.

    Older: check current guidance
  7. Teaching or conference talk

    Data Mining (Spring 2023) - PageRank Part II

    UofU Data Science

    Basic PageRank gets stuck on spider traps and dead ends. This data mining lecture shows how teleportation gets round both, then builds up to the final algorithm.

    Older: check current guidance
  8. Official source

    Curing crawling chaos

    Google Search Central

    Crystal Carter talks with Martin Splitt about a site that no crawling tool could crawl fully, the steps and tools used to fix it, and how monitoring can head it off.

    Older: check current guidance
  9. Teaching or conference talk

    IR Lecture 10: Crawlers

    Venkatesh Vinayakarao

    Web crawlers are responsible for a large share of internet traffic, according to this lecture, which explains how they work, why crawling is hard and gives a short look at Nutch.

    Older: check current guidance
  10. Teaching or conference talk

    IR Course Lecture 16: Popularity - Page Rank and HITS

    Venkatesh Vinayakarao

    PageRank and HITS are two classic ways to score how important a web page is, and this lecture works through both, including the random surfer idea and Markov chains.

    Older: check current guidance
  11. Official source

    Canonicalization: SEO Mythbusting

    Google Search Central

    Is canonicalisation a directive or a signal? Martin Splitt and Rachel Costello work through common myths about it, with timestamps to jump to each question.

    Older: check current guidance
  12. Official source

    Debugging JavaScript SEO issues

    Chrome for Developers

    Which steps help diagnose common JavaScript SEO problems? Martin Splitt of Google goes through them in a short web.dev LIVE session.

    Older: check current guidance
  13. Teaching or conference talk

    How Google's PageRank Algorithm Works

    Spanning Tree

    What makes a web page important to PageRank? This short video explains the random surfer idea and what the damping factor is for.

    Older: check current guidance
  14. Teaching or conference talk

    M4ML - Linear Algebra - 5.7 Introduction to PageRank

    Digital Media - Imperial College London

    An Imperial College London lecturer introduces PageRank through linear algebra, in week 5 of a Mathematics for Machine Learning course on Coursera.

    Older: check current guidance