FHSM: Factored Hybrid Similarity Methods for Top-N Recommender Systems

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• 关键词：Recommender systems ; Collaborative filtering ; Low ; rank ; Matrix factorization
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9932
• 期：1
• 页码：98-110
• 全文大小：1,207 KB
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• 作者单位：Xin Xin (17)
  Dong Wang (17)
  Yue Ding (17)
  Chen Lini (17)
  
  17. School of Software, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China
  
• 丛书名：Web Technologies and Applications
• ISBN：978-3-319-45817-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9932

文摘

Collaborative filtering (CF)-based methods in recommender systems believe that the user’s preference of an item is the aggregation of the similar items or users. However, conventional item-based or user-based 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-of-the-art methods.