An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering

• Published in: IEEE transactions on automation science and engineering Vol. 13; no. 1; pp. 333 - 343
• Main Authors: Luo, Xin, Zhou, MengChu, Leung, Hareton, Xia, Yunni, Zhu, Qingsheng, You, Zhuhong, Li, Shuai
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceptability; Accuracy; Adaptation models; Collaborative filtering; Computation; Computational modeling; Experiments; Factorization; Filtering; Filtration; incremental model; Mathematical models; Matrix; matrix factorization; Predictions; Predictive models; recommender system; Recommender systems; Scalability; scheme; static model; Training; Vectors
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2014.2348555

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.