Stock Image

Similarity Function with Temporal Factor in Collaborative Filtering

Meghna Khatri

ISBN 10: 3659179957 / ISBN 13: 9783659179952
Published by LAP LAMBERT Academic Publishing
New Condition: New Soft cover
From BuySomeBooks (Las Vegas, NV, U.S.A.)

AbeBooks Seller Since May 21, 2012

Quantity Available: 20

About this Item

Paperback. 56 pages. Dimensions: 8.7in. x 5.9in. x 0.1in.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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783659179952

Ask Seller a Question

Bibliographic Details

Title: Similarity Function with Temporal Factor in ...

Publisher: LAP LAMBERT Academic Publishing

Binding: Paperback

Book Condition:New

Book Type: Paperback

About this title

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.

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.

"About this title" may belong to another edition of this title.

Store Description

BuySomeBooks is great place to get your books online. With over eight million titles available we're sure to have what you're looking for. Despite having a large selection of new books available for immediate shipment and excellent customer service, people still tell us they prefer us because of our prices.

Visit Seller's Storefront

Terms of Sale:

We guarantee the condition of every book as it's described on the Abebooks web
sites. If you're dissatisfied with your purchase (Incorrect Book/Not as
Described/Damaged) or if the order hasn't arrived, you're eligible for a refund
within 30 days of the estimated delivery date. If you've changed your mind about a book that you've ordered, please use the Ask bookseller a question link to contact us and we'll respond within 2 business days.

BuySomeBooks is operated by Drive-On-In, Inc., a Nevada co...

More Information
Shipping Terms:

Orders usually ship within 1-2 business days. Books are shipped from multiple locations so your order may arrive from Las Vegas,NV, Roseburg,OR, La Vergne,TN, Momence,IL, or Commerce,GA.

List this Seller's Books

Payment Methods
accepted by seller

Visa Mastercard American Express