News Recommendations using CF-IDF

Frederik Hogenboom; Flavius Frasincar; U Kaymak; Franciska de Jong

doi:10.1145/1988688.1988701

Abstract

Most of the traditional recommendation algorithms are based on TF-IDF, a term-based weighting method. This paper proposes a new method for recommending news items based on the weighting of the occurrences of references to concepts, which we call Concept Frequency-Inverse Document Frequency (CF-IDF). In an experimental setup we apply CF-IDF to a set of newswires in which we detect 1,167 instances of a set of 65 concepts from a domain ontology. The proposed method yields significantly better results with respect to accuracy, recall, and F1 than the TF-IDF method we use as a basis for comparison.

Original language	English
Pages	397-398
Number of pages	2
DOIs	https://doi.org/10.1145/1988688.1988701
Publication status	Published - 2 Nov 2011
Event	Twenty-Third Benelux Conference on Artificial Intelligence (BNAIC 2011) - Gent, Belgium Duration: 2 Nov 2011 → 3 Nov 2011

Conference

Conference	Twenty-Third Benelux Conference on Artificial Intelligence (BNAIC 2011)
City	Gent, Belgium
Period	2/11/11 → 3/11/11

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10.1145/1988688.1988701

Cite this

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title = "News Recommendations using CF-IDF",

abstract = "Most of the traditional recommendation algorithms are based on TF-IDF, a term-based weighting method. This paper proposes a new method for recommending news items based on the weighting of the occurrences of references to concepts, which we call Concept Frequency-Inverse Document Frequency (CF-IDF). In an experimental setup we apply CF-IDF to a set of newswires in which we detect 1,167 instances of a set of 65 concepts from a domain ontology. The proposed method yields significantly better results with respect to accuracy, recall, and F1 than the TF-IDF method we use as a basis for comparison.",

author = "Frederik Hogenboom and Flavius Frasincar and U Kaymak and {de Jong}, Franciska",

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doi = "10.1145/1988688.1988701",

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AU - Hogenboom, Frederik

AU - Frasincar, Flavius

AU - Kaymak, U

AU - de Jong, Franciska

PY - 2011/11/2

Y1 - 2011/11/2

N2 - Most of the traditional recommendation algorithms are based on TF-IDF, a term-based weighting method. This paper proposes a new method for recommending news items based on the weighting of the occurrences of references to concepts, which we call Concept Frequency-Inverse Document Frequency (CF-IDF). In an experimental setup we apply CF-IDF to a set of newswires in which we detect 1,167 instances of a set of 65 concepts from a domain ontology. The proposed method yields significantly better results with respect to accuracy, recall, and F1 than the TF-IDF method we use as a basis for comparison.

AB - Most of the traditional recommendation algorithms are based on TF-IDF, a term-based weighting method. This paper proposes a new method for recommending news items based on the weighting of the occurrences of references to concepts, which we call Concept Frequency-Inverse Document Frequency (CF-IDF). In an experimental setup we apply CF-IDF to a set of newswires in which we detect 1,167 instances of a set of 65 concepts from a domain ontology. The proposed method yields significantly better results with respect to accuracy, recall, and F1 than the TF-IDF method we use as a basis for comparison.

U2 - 10.1145/1988688.1988701

DO - 10.1145/1988688.1988701

M3 - Abstract

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T2 - Twenty-Third Benelux Conference on Artificial Intelligence (BNAIC 2011)

Y2 - 2 November 2011 through 3 November 2011

ER -

News Recommendations using CF-IDF

Abstract

Conference

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