An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering
An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering
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DOI：
10.1109/tase.2014.2348555
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发表时间：
2016
影响因子：
5.6
通讯作者：
Xin Luo;Mengchu Zhou;Hareton K. N. Leung;Yunni Xia;Qingsheng Zhu;Zhu-Hong You;Shuai Li
中科院分区：
文献类型：
--
作者：
Xin Luo;Mengchu Zhou;Hareton K. N. Leung;Yunni Xia;Qingsheng Zhu;Zhu-Hong You;Shuai Li
基于协同过滤（CF）的推荐器通过矩阵分解（MF）来实现，以获得高预测精度和可扩展性。然而，当前大多数基于 MF 的模型都是静态模型，无法适应增量的用户反馈。这项工作旨在为基于 MF 的 CF 开发一种通用的、增量和静态组合的方案，以获得高度准确且计算上可负担的增量推荐器。有了它，推荐器被设计为由两个组件组成，即基于静态评级数据的静态组件和基于仅与评级变化相关的子矩阵构建的增量组件。因此，通过融合它们的结果可以生成高度可靠的预测。在大型工业数据集上的实验表明，使用所提出的方案所得到的推荐器可以实现所需的准确性和可接受的计算复杂性。
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.