Home           Contact us           FAQs           
 
   Journal Page   |   Aims & Scope   |   Author Guideline   |   Editorial Board   |   Search
    Abstract
2013 (Vol. 5, Issue: 16)
Article Information:

Collaborative Filtering Recommender Systems

Mehrbakhsh Nilashi, Karamollah Bagherifard, Othman Ibrahim, Hamid Alizadeh, Lasisi Ayodele Nojeem and Nazanin Roozegar
Corresponding Author:  Mehrbakhsh Nilashi 

Key words:  Collaborative filtering, item-based, prediction, rating, recommender system, user-based, recommendation,
Vol. 5 , (16): 4168-4182
Submitted Accepted Published
August 16, 2012 December 01, 2012 April 30, 2013
Abstract:

Recommender Systems are software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. The suggestions provided are aimed at support users in various decision-making processes. Technically, recommender system has their origins in different fields such as Information Retrieval (IR), text classification, machine learning and Decision Support Systems (DSS). Recommender systems are used to address the Information Overload (IO) problem by recommending potentially interesting or useful items to users. They have proven to be worthy tools for online users to deal with the IO and have become one of the most popular and powerful tools in E-commerce. Many existing recommender systems rely on the Collaborative Filtering (CF) and have been extensively used in E-commerce .They have proven to be very effective with powerful techniques in many famous E-commerce companies. This study presents an overview of the field of recommender systems with current generation of recommendation methods and examines comprehensively CF systems with its algorithms.
Abstract PDF HTML
  Cite this Reference:
Mehrbakhsh Nilashi, Karamollah Bagherifard, Othman Ibrahim, Hamid Alizadeh, Lasisi Ayodele Nojeem and Nazanin Roozegar, 2013. Collaborative Filtering Recommender Systems.  Research Journal of Applied Sciences, Engineering and Technology, 5(16): 4168-4182.
    Advertise with us
 
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Current Information
   Sales & Services
   Contact Information
  Executive Managing Editor
  Email: admin@maxwellsci.com
  Publishing Editor
  Email: support@maxwellsci.com
  Account Manager
  Email: faisalm@maxwellsci.com
  Journal Editor
  Email: admin@maxwellsci.com
  Press Department
  Email: press@maxwellsci.com
Home  |  Contact us  |  About us  |  Privacy Policy
Copyright © 2009. MAXWELL Science Publication, a division of MAXWELLl Scientific Organization. All rights reserved