Title :
A Hybrid Collaborative Filtering Recommendation Algorithm for Solving the Data Sparsity
Author :
He, Ying ; Yang, Shaoyu ; Jiao, Chenbin
Author_Institution :
Henan Bus. Coll., Zhengzhou, China
Abstract :
With the huge electronic data´s explosion in the commercial and the service area, the collaborative filtering technology attracts many of researchers´ attention. In this paper, we provide a hybrid collaborative filtering recommendation algorithm, which based on the research and analyses for the data sparsity and the similarity accuracy. The simulation result indicates that the algorism can solve effectively the extreme data sparsity and promote the similarity accuracy in collaborative filtering.
Keywords :
data handling; information filtering; recommender systems; data sparsity; electronic data explosion; hybrid collaborative filtering recommendation algorithm; Accuracy; Bayesian methods; Collaboration; Data models; Filling; Filtering; Predictive models; collaborative filtering algorism [CFA]; data sparsity; recommend system [RS];
Conference_Titel :
Computer Science and Society (ISCCS), 2011 International Symposium on
Conference_Location :
Kota Kinabalu
Print_ISBN :
978-1-4577-0644-8
DOI :
10.1109/ISCCS.2011.40