Similarity Function With Temporal Factor In Collaborative Filtering : Data Mining

Language: English

Published by LAP LAMBERT Academic Publishing, 2012

3659179957 / 9783659179952

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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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

Title
Similarity Function With Temporal Factor In Collaborative Filtering : Data Mining
Author
Meghna Khatri
Publisher
LAP LAMBERT Academic Publishing
Publication year
2012
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3659179957
ISBN 13
9783659179952
Item weight
102 grams
Dimensions
220x150x4 mm

AHA-BUCH GmbH

Einbeck, Germany

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