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:
File | Description | Size | Format | |
---|---|---|---|---|
6883239.pdf | 1.87 MB | Adobe PDF | ![]() 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 | Size | Format | |
---|---|---|---|---|
6883239.pdf | 1.87 MB | Adobe PDF | ![]() 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 | Size | Format | |
---|---|---|---|---|
6883239.pdf | 1.87 MB | Adobe PDF | ![]() Preview File |