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72match
kjskooking.com
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3 co-citations4 shared tech1 shared categoryfood-and-drink
68match
celestialcocktail.com
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2 co-citations7 shared tech1 shared categoryfood-and-drink
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2 co-citations7 shared tech1 shared categoryfood-and-drink
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63match
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2 co-citations8 shared tech1 shared categoryalcoholic-beverages
63match
elguanacoauburnhills.com
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3 co-citations0 shared tech2 shared categoryfood-and-drink
63match
elguanacotroy.com
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3 co-citations0 shared tech2 shared categoryfood-and-drink
63match
greatmindscoffee.com
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2 co-citations7 shared tech2 shared categorynon-alcoholic-beverages
63match
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59match
kanakoscakes.com
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2 co-citations7 shared tech0 shared categorydesserts-and-baking
57match
joe2golv.com
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2 co-citations7 shared tech3 shared categorynon-alcoholic-beverages
57match
booniehatcoffee.com
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2 co-citations7 shared tech2 shared categorynon-alcoholic-beverages
57match
cebuanafoods.com
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2 co-citations2 shared tech2 shared categoryfood-and-drink
56match
grazebyliza.com
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2 co-citations7 shared tech1 shared categoryfood-and-drink
56match
canadahoneydo.com
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2 co-citations4 shared tech2 shared categoryfood-and-drink
56match
coremagin.com
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2 co-citations7 shared tech0 shared categoryalcoholic-beverages
55match
hoianroastery.com
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2 co-citations2 shared tech2 shared categoryfood-and-drink

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.