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Matt Leaverton · ranked by shared content topics & relevance

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

Home | Matt Joseph

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

1 shared topicstechnology-and-computing

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.