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An intelligent multi-agent recommender system / Mahmoud Abdelmoneim Mahmood ; Supervised Aboulella Hassanien , Hesham Ahmed Hefny , Nashwa Elbendary

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mahmoud Abdelmoneim Mahmood , 2014Description: 132 P. : charts ; 30cmOther title:
  • نظام توصية ذكى متعدد الوكلاء [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Sciences Summary: For many users of information systems, 3information overload3 has become a problem: The amount of information they must sift through has reached the point where it is overwhelming. Recommender systems are intelligent tools that help on-line users to tame information overload. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user{u2019}s needs based on data coming from similar users, are becoming increasingly popular as ways to combat this information overload. While accuracy has been a major focus of CF, in practice, efficiency, data sparsity, scalability, and cold start problem are also important issues in CF in order to enhance the performance of CF systems. Efficiency refers to the cost of CF algorithms. We propose an approach that is able to predict what information will meet a user{u2019}s needs based on data coming from similar users with accepted accuracy to user
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.Ph.D.2014.Ma.I (Browse shelf(Opens below)) Not for loan 01010110064962000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.Ph.D.2014.Ma.I (Browse shelf(Opens below)) 64962.CD Not for loan 01020110064962000

Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Sciences

For many users of information systems, 3information overload3 has become a problem: The amount of information they must sift through has reached the point where it is overwhelming. Recommender systems are intelligent tools that help on-line users to tame information overload. Collaborative Filtering (CF) techniques, which attempt to predict what information will meet a user{u2019}s needs based on data coming from similar users, are becoming increasingly popular as ways to combat this information overload. While accuracy has been a major focus of CF, in practice, efficiency, data sparsity, scalability, and cold start problem are also important issues in CF in order to enhance the performance of CF systems. Efficiency refers to the cost of CF algorithms. We propose an approach that is able to predict what information will meet a user{u2019}s needs based on data coming from similar users with accepted accuracy to user

Issued also as CD

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