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News Recommendation Using Term Frequency and Document Similarity

Name: News Recommendation Using Term Frequency and Document Similarity
Brand: LAP Lambert Academic Publishing
SKU: 9783659949760
Price: 754 CZK
Availability: InStock

Autor: Ashwini Gupta, Vaibhav Jain

Jazyk:

Vazba: Brožovaná

Vydavatel: LAP Lambert Academic Publishing

Dostupnost: Skladem u dodavatele

Odesíláme za 5-8 dnů

754 Kč

Excessive overloading of information has become a serious problem recently. Extensive use of technol...

Informace o knize

Autor

Ashwini Gupta, Vaibhav Jain

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2016

Stránek

EAN

9783659949760

Enbook ID

15200979

Vydavatel

LAP Lambert Academic Publishing

Hmotnost

Rozměry

150 x 220 x 4

Kompletní popis

Excessive overloading of information has become a serious problem recently. Extensive use of technology has made life easier but it also lead to access of information creation. There are several news portals where lots of information gets uploaded daily. As it is an era of E-News where online news reading has become a common habit of people. People are more likely to read News on Web rather than on Newspaper or other media. It becomes harder for user to find relevant and popular news in small time. Now a day it has become a key challenge as everyone has different liking and reading habits. A solution to this problem is news recommendation system. A Content Based Recommendation is developed which recommends news on the basis of article similarity with query and document similarity. Measures like term frequency count & document similarity are used to find out the similarity of query in the complete corpus of News articles. Each document is compared with every document available in corpus and content matching is performed to find out the similarity score. Results are evaluated on two different datasets using measures are used to evaluate the relevancy of recommended News articles.

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News Recommendation Using Term Frequency and Document Similarity

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