Employing rough set theory to alleviate the sparsity issue in recommender system

Recommender systems represent personalized services that aim at predicting a userpsilas interest on information items available in the application domain, using userspsila ratings on items. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of userspsila rat...

Full description

Saved in:
Bibliographic Details
Published in2008 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 1610 - 1614
Main Authors Chong-Ben Huang, Song-Jie Gong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2008
Subjects
Accuracy
Collaboration
Collaborative filtering
Correlation
Filtering
Information systems
Prediction algorithms
Recommender system
Rough set theory
Set theory
Sparsity
Online AccessGet full text
ISBN1424420954
9781424420957
ISSN2160-133X
DOI10.1109/ICMLC.2008.4620663

Cover

More Information
Summary:Recommender systems represent personalized services that aim at predicting a userpsilas interest on information items available in the application domain, using userspsila ratings on items. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of userspsila ratings is the major reason causing the poor quality. The popular same value and singular value decomposition techniques are able to alleviate this issue. But they also introduce new problems. A collaborative filtering based on rough set theory was proposed to solve this problem, which predicts values of the null ratings in the candidates, and gets the results using userpsilas neighbors. Experimental results show that this method can increase the accuracy of the predicted values, resulting in improving recommendation quality of the collaborative filtering recommender system.
ISBN:1424420954
9781424420957
ISSN:2160-133X
DOI:10.1109/ICMLC.2008.4620663