Applying the learning rate adaptation to the matrix factorization based collaborative filtering

详细信息    查看全文

• 作者：Xin Luo ; ^{luoxin21@cqu.edu.cn} ; Yunni Xia ^{xiayunni@yahoo.com.cn} ; Qingsheng Zhu ^{qszhu@cqu.edu.cn}
• 关键词：Recommender system ; Collaborative filtering ; Matrix factorization ; Learning rate adaptation ; Latent Factor
• 刊名：Knowledge-Based Systems
• 出版年：2013
• 出版时间：January, 2013
• 年：2013
• 卷：37
• 期：Complete
• 页码：154-164
• 全文大小：947 K

文摘

Matrix Factorization (MF) based Collaborative Filtering (CF) have proved to be a highly accurate and scalable approach to recommender systems. In MF based CF, the learning rate is a key factor affecting the recommendation accuracy and convergence rate; however, this essential parameter is difficult to decide, since the recommender has to keep the balance between the recommendation accuracy and convergence rate. In this work, we choose the Regularized Matrix Factorization (RMF) based CF as the base model to discuss the effect of the learning rate in MF based CF, trying to deal with the dilemma of learning rate tuning through learning rate adaptation. First of all, we empirically validate the affection caused by the change of the learning rate on the recommendation performance. Subsequently, we integrate three sophisticated learning rate adapting strategies into RMF, including the Deterministic Step Size Adaption (DSSA), the Incremental Delta Bar Delta (IDBD), and the Stochastic Meta Decent (SMD). Thereafter, by analyzing the characteristics of the parameter update in RMF, we further propose the Gradient Cosine Adaption (GCA). The experimental results on five public large datasets demonstrate that by employing GCA, RMF could maintain good balance between accuracy and convergence rate, especially with small learning rate values.