自动化学报Issue(2)：405-418,14.DOI:10.16383/j.aas.2015.c140231
鲁棒的单类协同排序算法
Robust Ranking Algorithms for One-class Collaborative Filtering
摘要
Abstract
The problem of ranking for one-class collaborative filtering (OCCF) is a research focus. One drawback of the existing ranking algorithms for OCCF is noise sensitivity, because the noisy data of training data might bring big influences to the training process and lead to inaccuracy of the algorithm. In this paper, in order to solve the noise sensitivity problem of the ranking algorithms, we propose two robust ranking algorithms for OCCF by using the pairwise sigmoid/fidelity loss functions that are flexible and can be easily adopted by the popular matrix factorization (MF) model and the K-nearest-neighbor (KNN) model. We use stochastic gradient descent with bootstrap sampling to optimize the two robust ranking algorithms. Experimental results on three practical datasets containing a large number of noisy data show that our proposed algorithms outperform several state-of-the-art ranking algorithms for OCCF in terms of different evaluation metrics.关键词
推荐系统/单类协同过滤/协同排序/隐式反馈/成对损失函数Key words
Recommender systems/one-class collaborative filtering (OCCF)/collaborative ranking/implicit feedback/pairwise loss function引用本文复制引用
李改,李磊..鲁棒的单类协同排序算法[J].自动化学报,2015,(2):405-418,14.基金项目
国家自然科学基金(61003140,61033010),中山大学高性能与网格计算平台资助@@@@Supported by National Natural Science Foundation of China (61003140,61033010), and High Performance and Grid Com-puting Platform of Sun Yat-sen University （61003140,61033010）
