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Amazon・楽天市場の特価品をAIで分析 - 欲しい商品は特価で買う【バカヤス】 · ranked by category · tech stack · backlink co-citation
28match
matoyoko.com
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0 co-citations1 shared tech2 shared categorycoupons-and-discounts
26match
costcolove.com
ao LOVE costco !!
0 co-citations2 shared tech2 shared categorygrocery-shopping
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fightact.com
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0 co-citations2 shared tech2 shared categorysales-and-promotions
25match
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0 co-citations3 shared tech1 shared categorycoupons-and-discounts
25match
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25match
baicho.com
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0 co-citations2 shared tech1 shared categoryshopping
25match
hami-okaimono.com
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0 co-citations2 shared tech2 shared categorycoupons-and-discounts
24match
bazzpocket.com
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0 co-citations2 shared tech2 shared categorycoupons-and-discounts
24match
diasaro.com
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0 co-citations2 shared tech1 shared categoryhousehold-supplies
23match
banggood-coupon.com
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22match
aesquare.com
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0 co-citations1 shared tech1 shared categoryshopping
20match
bonusmee.com
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0 co-citations0 shared tech2 shared categorycoupons-and-discounts
20match
kyou-toku-gohan.com
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0 co-citations2 shared tech1 shared categorycoupons-and-discounts
20match
japanesereviews.com
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20match
jeffosretromusic.com
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0 co-citations0 shared tech2 shared categorycoupons-and-discounts
20match
matsudostudio.com
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0 co-citations0 shared tech2 shared categoryshopping
20match
hayatsondaj.com
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0 co-citations0 shared tech2 shared categoryshopping
20match
aoyamamarket.com
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0 co-citations1 shared tech1 shared categoryshopping

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.