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58match
bookalistic.com
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0 co-citations3 shared tech1 shared categorybooks-and-literature
58match
booksjk.com
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0 co-citations3 shared tech1 shared categoryfiction
50match
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0 co-citations2 shared tech1 shared categoryfiction
50match
gerrihill.com
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0 co-citations3 shared tech1 shared categorybooks-and-literature
50match
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0 co-citations2 shared tech1 shared categorybooks-and-literature
48match
greatbeachbooks.com
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46match
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0 co-citations3 shared tech1 shared categorybooks-and-literature
46match
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0 co-citations3 shared tech1 shared categorybooks-and-literature
45match
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0 co-citations2 shared tech1 shared categoryfiction
45match
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0 co-citations3 shared tech1 shared categorybooks-and-literature
45match
jack-king.com
Jack King | Home
0 co-citations3 shared tech1 shared categoryfiction
44match
bookmakerindustries.com
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0 co-citations3 shared tech1 shared categoryfiction
44match
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0 co-citations3 shared tech1 shared categorybooks-and-literature
44match
boosteranthology.com
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44match
centralia-library.com
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0 co-citations3 shared tech1 shared categorybooks-and-literature
44match
keoboypublications.com
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0 co-citations2 shared tech1 shared categorybooks-and-literature
44match
kenziemack.com
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44match
jacobgowans.com
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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:

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.