uncleflo
Registered since September 28th, 2017
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NatWest to convert all debit cards to Mastercard
Saved by uncleflo on January 16th, 2022.
NatWest is to convert all of its debit cards to Mastercard, giving the card scheme a massive leg-up in its efforts to break the UK dominance of rival Visa. While this conversion may "give the card scheme a massive leg-up in its efforts to break the UK dominance of rival Visa", it does little to improve competion or result in lower card acceptance costs for merchants. While the interchange fee regulation caps fees at 0.2% for debit cards, the Mastercard card scheme fees are significantly higher than those of Visa so will result in much higher costs for merchants - and ultimately consumers. This is not good news. Also, as the interchange fees are capped, there is little incentive for card issuers to switch brands as their income remains the same - unless a 'behind the scene' deal has been done to sweeten the deal - which would (in effect) be contrary to the anti-circumvention clause of the IFR.
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tf–idf - Wikipedia
Saved by uncleflo on December 23rd, 2018.
In information retrieval, tf–idf or TFIDF, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. The tf–idf value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general. Tf–idf is one of the most popular term-weighting schemes today; 83% of text-based recommender systems in digital libraries use tf–idf. Variations of the tf–idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document's relevance given a user query. tf–idf can be successfully used for stop-words filtering in various subject fields, including text summarization and classification. One of the simplest ranking functions is computed by summing the tf–idf for each query term; many more sophisticated ranking functions are variants of this simple model.
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