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* ([[Pazzani & Billsus, 2007]]) ⇒ [[Michael J. Pazzani]], and Daniel Billsus. "Content-based recommendation systems." In The adaptive web, pp. 325-341. Springer Berlin Heidelberg, 2007. | * ([[Pazzani & Billsus, 2007]]) ⇒ [[Michael J. Pazzani]], and Daniel Billsus. "Content-based recommendation systems." In The adaptive web, pp. 325-341. Springer Berlin Heidelberg, 2007. | ||
** QUOTE: ... associated with a [[text term|term]] is a [[real number score|real number]] that represents the [[importance or relevance]]. This value is called the [[tf*idf weight]] ([[term-frequency times inverse document frequency]]). The [[tf*idf weight]], w(t,d), of a term t in a document d is a function of the frequency of t in the document (tft,d), the number of documents that contain the term (dft) and the number of documents in the collection (N)<ref>Note that in the description of [[tf*idf weight]]s, the word “document” is traditionally used since the original motivation was to [[retrieve documents]]. While the chapter will stick with the original terminology, in a recommendation system, the documents correspond to a text description of an item to be recommended. Note that the equations here are representative of the class of formulae called [[tf*idf]]. In general, [[tf*idf system]]s have weights that increase monotonically with [[term frequency]] and decrease monotonically with [[document frequency]].</ref> | ** QUOTE: ... associated with a [[text term|term]] is a [[real number score|real number]] that represents the [[importance or relevance]]. This value is called the [[tf*idf weight]] ([[term-frequency times inverse document frequency]]). The [[tf*idf weight]], w(t,d), of a term t in a document d is a function of the frequency of t in the document (tft,d), the number of documents that contain the term (dft) and the number of documents in the collection (N)<ref>Note that in the description of [[tf*idf weight]]s, the word “document” is traditionally used since the original motivation was to [[retrieve documents]]. While the chapter will stick with the original terminology, in a recommendation system, the documents correspond to a text description of an item to be recommended. Note that the equations here are representative of the class of formulae called [[tf-idf Scoring Function|tf*idf]]. In general, [[tf*idf system]]s have weights that increase monotonically with [[term frequency]] and decrease monotonically with [[document frequency]].</ref> | ||
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