Recommender systems based on ranking performance optimization

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• 作者：Richong Zhang ; Han Bao ; Hailong Sun ; Yanghao Wang…
• 关键词：recommender system ; matrix factorization ; learning to rank
• 刊名：Frontiers of Computer Science in China
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：10
• 期：2
• 页码：270-280
• 全文大小：585 KB
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• 作者单位：Richong Zhang (1)
  Han Bao (1)
  Hailong Sun (1)
  Yanghao Wang (1)
  Xudong Liu (1)
  
  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China
  
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

The rapid development of online services and information overload has inspired the fast development of recommender systems, among which collaborative filtering algorithms and model-based recommendation approaches are wildly exploited. For instance, matrix factorization (MF) demonstrated successful achievements and advantages in assisting internet users in finding interested information. These existing models focus on the prediction of the users’ ratings on unknown items. The performance is usually evaluated by the metric root mean square error (RMSE). However, achieving good performance in terms of RMSE does not always guarantee a good ranking performance. Therefore, in this paper, we advocate to treat the recommendation as a ranking problem. Normalized discounted cumulative gain (NDCG) is chosen as the optimization target when evaluating the ranking accuracy. Specifically, we present three ranking-oriented recommender algorithms, NSMF, AdaMF and AdaNSMF. NSMF builds a NDCG approximated loss function for Matrix Factorization. AdaMF is based on an algorithm by adaptively combining component MF recommenders with boosting method. To combine the advantages of both algorithms, we propose AdaNSMF, which is a hybird of NSMF and AdaMF, and show the superiority in both ranking accuracy and model generalization. In addition, we compare our proposed approaches with the state-of-the-art recommendation algorithms. The comparison studies confirm the advantage of our proposed approaches.