FHSM:Factored Hybrid Similarity Methods for Top-N Recommender Systems
Collaborative filtering (CF)-based methods in recommender systems believe that the users preference of an item is the aggregation of the similar items or users.However,conventional item-based or userbased CF methods only consider either the item similarity or the user similarity.In this paper,we present hybrid-based methods for generating top-N recommendations in which both the item-item and user-user similarities are captured by the dot product of two low dimensional latent factor matrices.These matrices are learned using a stochastic gradient descent (SGD) algorithm to minimize two different loss functions,one is the squared error loss function and the other is the logistic loss function.A comprehensive set of experiments on multiple datasets is conducted to evaluate the performance of the proposed methods.The experimental results demonstrate the factored hybrid similarity methods (FHSM) achieve a superior recommendation quality in comparison with state-ofthe-art methods.
Recommender systems Collaborative filtering Low-rank Matrix factorization
Xin Xin Dong Wang Yue Ding Chen Lini
School of Software,Shanghai Jiao Tong University,800 Dongchuan Road,Shanghai,China
国际会议
International Asia-Pacific Web Conference(第18届国际亚太互联网大会)
苏州
英文
98-110
2016-09-23（万方平台首次上网日期，不代表论文的发表时间）