Using K-means Clustering and Similarity Measure to Deal with Missing Rating in Collaborative Filtering Recommendation Systems

The Collaborative Filtering recommendation systems have been developed to address the information overload problem and personalize the content to the users for business and organizations. However, the Collaborative Filtering approach has its limitation of data sparsity and online scalability problems which result in low recommendation quality. In this thesis, a novel Collaborative Filtering approach is introduced using clustering and similarity technologies. The proposed method using K-means clustering to partition the entire dataset reduces the time complexity and improves the online scalability as well as the data density. Moreover, the similarity comparison method predicts and fills up the missing value in sparsity dataset to enhance the data density which boosts the recommendation quality. This thesis uses MovieLens dataset to investigate the proposed method, which yields amazing experimental outcome on a large sparsity data set that has a higher quality with lower time complexity than the traditional Collaborative Filtering approaches.

Author dc:creator

• Xiong, Chenrui

Advisor dc:contributor.advisor

• Yang, Zijiang Cynthia

• Information technology

Statement dc:rights

• Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10315/34294

OAI identifier oai:identifier

oai:yorkspace.library.yorku.ca:10315/34294