Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data-Reference-Cited by-同舟云学术

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Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data

Published:2017-02 Issue: Volume:67 Page:113-128
ISSN:0747-5632
Container-title:Computers in Human Behavior
language:en
Short-container-title:Computers in Human Behavior

Author:

Najafabadi Maryam Khanian,Mahrin Mohd Naz'ri,Chuprat Suriayati,Sarkan Haslina Md

Funder

Universiti Teknologi Malaysia (UTM)

Publisher

Elsevier BV

Subject

General Psychology,Human-Computer Interaction,Arts and Humanities (miscellaneous)

Reference45 articles.

1. A collaborative filtering method based on artificial immune network;Acilar;Expert Systems with Applications,2009

2. Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities;Anand;Expert systems with applications,2011

3. Recommender systems based on quantitative implicit customer feedback;Bauer;Decision Support Systems,2014

4. Generalization of recommender systems: Collaborative filtering extended to groups of users and restricted to groups of items;Bobadilla;Expert systems with Applications,2012

5. Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering;Boratto;Future Generation Computer Systems,2015

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