Amazon uses trained random forests of decision trees to incorporate thousands of product attributes at rank time.
Unresolvable The public record could not settle it.
Factual: Statements about the present or the past, checked against the record: did they hold up?
Said for an organisation they lead or work for; it counts on both records.
What would count as true or false
True: there is evidence that around 2011 Amazon's product search or ranking used trained random-forest or decision-tree-ensemble models that incorporate many product attributes at ranking time. Mostly true: Amazon used tree-ensemble machine-learning ranking (for example gradient-boosted trees) with many features, but the exact method or the attribute count differs slightly. Mostly false: Amazon used machine-learned ranking but not tree ensembles, or only on a small scale. False: the evidence shows Amazon did not use such techniques at the time. If the only source is Amazon's own statement, the claim is consistent with it, but some independent or technical corroboration (A9 papers, engineers' talks, job postings) is preferred.
Set before the research began.
Evidence (3)
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E1 Amazon's Secret Ranking Algorithm Exposed: How I Tricked an A.I. into Revealing the Hidden Truth! Signalytics · 27 March 2023
“Ranking Models: Gradient boosted trees are used as the main method for ranking because they can discover complex feature interactions and work well without significant tuning.”
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E2 Amazon's Secret Ranking Algorithm Exposed: How I Tricked an A.I. into Revealing the Hidden Truth! Signalytics · 27 March 2023
“Amazon has over a hundred machine-learned models in their system, with each model using about 20 of the 150 available features.”
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E3 The 8 Best Papers on eCommerce Search Algorithms Constructor · 3 March 2020
“This paper dives deeper into the algorithms and ML frameworks Amazon’s A9 team have implemented to rank products in categories, blend separate rankings in All Product Search, implement NLP techniques used for matching queries and products, and more.”
Reasoning
E1 says gradient boosted trees are Amazon's main ranking method, and E2 says Amazon has over a hundred machine-learned models, each using about 20 of 150 available features. Neither quote gives a time period, so neither shows what Amazon used around 2011. The source for both is also weak: a 2023 blog post that says it got its information by prompting an AI. E3 only notes that a paper covers A9's ranking algorithms, without saying what methods were used or when, so the evidence does not settle how Amazon ranked products in 2011.
Cite this claim
Jeff Bezos, “Amazon uses trained random forests of decision trees to incorporate thousands of product attributes at rank time.” (said 2011). Verdict: unresolvable. Reliability API, claim c_a68cebaeec1c, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_a68cebaeec1c
This claim is in dataset release v1.1.2, with the same verdict.
Reply to this claim
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Verdicts are made automatically; how verdicts are made.