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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
Editore:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659949760
ISBN 13: 9783659949760
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta AshwiniMs Ashwini Gupta completed Masters in engineering from IET DAVV Indore in 2016. Dr. Vaibhav Jain is Asst. Professor at Inst. of Engg. & Technology Devi Ahilya Vishwavidyalaya Indore, India. He has obtained his PhD degre. Codice articolo 158249193
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News Recommendation Using Term Frequency and Document Similarity
Editore:
LAP Lambert Academic Publishing Okt 2016, 2016
ISBN 10: 3659949760
ISBN 13: 9783659949760
Nuovo
Taschenbuch
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 52 pp. Englisch. Codice articolo 9783659949760
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News Recommendation Using Term Frequency and Document Similarity
Editore:
LAP Lambert Academic Publishing, 2016
ISBN 10: 3659949760
ISBN 13: 9783659949760
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9783659949760
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News Recommendation Using Term Frequency and Document Similarity
Editore:
LAP Lambert Academic Publishing Okt 2016, 2016
ISBN 10: 3659949760
ISBN 13: 9783659949760
Nuovo
Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 52 pp. Englisch. Codice articolo 9783659949760
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News Recommendation Using Term Frequency and Document Similarity
Editore:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659949760
ISBN 13: 9783659949760
Nuovo
Paperback
Da: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. 52 pages. 8.66x5.91x0.12 inches. In Stock. Codice articolo 3659949760
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