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59match
elandorawilderness.com
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2 co-citations2 shared tech1 shared categoryafrica-travel
48match
balijeeptourtrekking.com
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2 co-citations2 shared tech1 shared categoryadventure-travel
47match
balidreaming.com
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2 co-citations2 shared tech0 shared categorytravel
46match
geomeandering.com
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46match
arranmoreglamping.com
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2 co-citations1 shared tech1 shared categorycamping
45match
maroonvoyagers.com
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0 co-citations3 shared tech0 shared categorytravel
44match
kangariver.com
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2 co-citations2 shared tech0 shared categoryafrica-travel
44match
fedindie.com
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1 co-citations2 shared tech0 shared categoryhotels-and-motels
43match
marlafay.com
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2 co-citations2 shared tech1 shared categorytravel
42match
corrincollina.com
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2 co-citations0 shared tech1 shared categoryadventure-travel
42match
devmanhotel.com
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2 co-citations1 shared tech0 shared categoryhotels-and-motels
41match
aeglamping.com
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costaazulziplines.com
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2 co-citations1 shared tech1 shared categoryadventure-travel
41match
adventurouslyyours.com
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1 co-citations2 shared tech1 shared categorytravel
40match
banjaratrailsindia.com
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2 co-citations2 shared tech1 shared categoryadventure-travel
40match
elfenwoodcamp.com
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1 co-citations1 shared tech1 shared categorycamping
40match
adventurewatersport.com
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39match
kennedymarinapark.com
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1 co-citations1 shared tech1 shared categorycamping

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.