Synopsis
Similarity function is the key to accuracy of collaborative filtering algorithms. Adding a time factor to it addresses the problem of handling the web data efficiently as it is highly dynamic in nature. The data used in collaborative filtering algorithms is collected over as long period of time, in the form of feedbacks, clicks, etc. The interest of user or popularity of an item tends to change as new seasons, moods or festivals. The similarity function with temporal factor can efficiently handle the dynamics of web data as it captures and assigns weightage to the data. More recent data is given more weightage when similarity is calculated. in this way, the recent trends and older and obsolete data values are discarded when new unobserved items are predicted using collaborative filtering algorithms. Hence, better results and more accuracy.
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About the Author
Meghna Khatri has obtained her Master's degree in Computer Science Engineering from Maharishi Dayanand University. Her main focus of research is collaborative filtering.She has several publications in international and national journals.Apart from academics, she has been an active participant in co-curricular activities at school and college level.
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Similarity Function With Temporal Factor In Collaborative Filtering
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LAP LAMBERT Academic Publishing Jul 2012, 2012
ISBN 10: 3659179957
ISBN 13: 9783659179952
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Similarity function is the key to accuracy of collaborative filtering algorithms. Adding a time factor to it addresses the problem of handling the web data efficiently as it is highly dynamic in nature. The data used in collaborative filtering algorithms is collected over as long period of time, in the form of feedbacks, clicks, etc. The interest of user or popularity of an item tends to change as new seasons, moods or festivals. The similarity function with temporal factor can efficiently handle the dynamics of web data as it captures and assigns weightage to the data. More recent data is given more weightage when similarity is calculated. in this way, the recent trends and older and obsolete data values are discarded when new unobserved items are predicted using collaborative filtering algorithms. Hence, better results and more accuracy. 56 pp. Englisch. Seller Inventory # 9783659179952
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Similarity Function With Temporal Factor In Collaborative Filtering
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Khatri MeghnaMeghna Khatri has obtained her Master s degree in Computer Science Engineering from Maharishi Dayanand University. Her main focus of research is collaborative filtering.She has several publications in international and n. Seller Inventory # 5137457
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Similarity Function With Temporal Factor In Collaborative Filtering
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LAP LAMBERT Academic Publishing Jul 2012, 2012
ISBN 10: 3659179957
ISBN 13: 9783659179952
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Similarity function is the key to accuracy of collaborative filtering algorithms. Adding a time factor to it addresses the problem of handling the web data efficiently as it is highly dynamic in nature. The data used in collaborative filtering algorithms is collected over as long period of time, in the form of feedbacks, clicks, etc. The interest of user or popularity of an item tends to change as new seasons, moods or festivals. The similarity function with temporal factor can efficiently handle the dynamics of web data as it captures and assigns weightage to the data. More recent data is given more weightage when similarity is calculated. in this way, the recent trends and older and obsolete data values are discarded when new unobserved items are predicted using collaborative filtering algorithms. Hence, better results and more accuracy.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch. Seller Inventory # 9783659179952
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Similarity Function With Temporal Factor In Collaborative Filtering : Data Mining
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Similarity function is the key to accuracy of collaborative filtering algorithms. Adding a time factor to it addresses the problem of handling the web data efficiently as it is highly dynamic in nature. The data used in collaborative filtering algorithms is collected over as long period of time, in the form of feedbacks, clicks, etc. The interest of user or popularity of an item tends to change as new seasons, moods or festivals. The similarity function with temporal factor can efficiently handle the dynamics of web data as it captures and assigns weightage to the data. More recent data is given more weightage when similarity is calculated. in this way, the recent trends and older and obsolete data values are discarded when new unobserved items are predicted using collaborative filtering algorithms. Hence, better results and more accuracy. Seller Inventory # 9783659179952
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Similarity Function With Temporal Factor In Collaborative Filtering | Data Mining
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Taschenbuch. Condition: Neu. Similarity Function With Temporal Factor In Collaborative Filtering | Data Mining | Meghna Khatri (u. a.) | Taschenbuch | 56 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659179952 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 106329340