Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/594155
Title: An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering
Authors: Xin Luo;MengChu Zhou;Hareton Leung;Yunni Xia;Qingsheng Zhu;Zhuhong You;Shuai Li
subject: matrix factorization;static model;incremental model;recommender system;Collaborative filtering;scheme
Year: 2016
Publisher: IEEE
Abstract: Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain high prediction accuracy and scalability. Most current MF-based models, however, are static ones that cannot adapt to incremental user feedbacks. This work aims to develop a general, incremental- and-static-combined scheme for MF-based CF to obtain highly accurate and computationally affordable incremental recommenders. With it, a recommender is designed to consist of two components, i.e., a static one built on static rating data, and an incremental one built on a sub-matrix related to rating-variations only. Highly reliable predictions are thus generated by fusing their results. The experiments on large industrial datasets show that desired accuracy and acceptable computational complexity are achieved by the resulting recommender with the proposed scheme.
URI: http://localhost/handle/Hannan/175689
http://localhost/handle/Hannan/594155
ISSN: 1545-5955
1558-3783
volume: 13
issue: 1
Appears in Collections:2016

Files in This Item:
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6883239.pdf1.87 MBAdobe PDFThumbnail
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Title: An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering
Authors: Xin Luo;MengChu Zhou;Hareton Leung;Yunni Xia;Qingsheng Zhu;Zhuhong You;Shuai Li
subject: matrix factorization;static model;incremental model;recommender system;Collaborative filtering;scheme
Year: 2016
Publisher: IEEE
Abstract: Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain high prediction accuracy and scalability. Most current MF-based models, however, are static ones that cannot adapt to incremental user feedbacks. This work aims to develop a general, incremental- and-static-combined scheme for MF-based CF to obtain highly accurate and computationally affordable incremental recommenders. With it, a recommender is designed to consist of two components, i.e., a static one built on static rating data, and an incremental one built on a sub-matrix related to rating-variations only. Highly reliable predictions are thus generated by fusing their results. The experiments on large industrial datasets show that desired accuracy and acceptable computational complexity are achieved by the resulting recommender with the proposed scheme.
URI: http://localhost/handle/Hannan/175689
http://localhost/handle/Hannan/594155
ISSN: 1545-5955
1558-3783
volume: 13
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
6883239.pdf1.87 MBAdobe PDFThumbnail
Preview File
Title: An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering
Authors: Xin Luo;MengChu Zhou;Hareton Leung;Yunni Xia;Qingsheng Zhu;Zhuhong You;Shuai Li
subject: matrix factorization;static model;incremental model;recommender system;Collaborative filtering;scheme
Year: 2016
Publisher: IEEE
Abstract: Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain high prediction accuracy and scalability. Most current MF-based models, however, are static ones that cannot adapt to incremental user feedbacks. This work aims to develop a general, incremental- and-static-combined scheme for MF-based CF to obtain highly accurate and computationally affordable incremental recommenders. With it, a recommender is designed to consist of two components, i.e., a static one built on static rating data, and an incremental one built on a sub-matrix related to rating-variations only. Highly reliable predictions are thus generated by fusing their results. The experiments on large industrial datasets show that desired accuracy and acceptable computational complexity are achieved by the resulting recommender with the proposed scheme.
URI: http://localhost/handle/Hannan/175689
http://localhost/handle/Hannan/594155
ISSN: 1545-5955
1558-3783
volume: 13
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
6883239.pdf1.87 MBAdobe PDFThumbnail
Preview File