Journal of Intelligent Information Systems

, Volume 47, Issue 2, pp 233–246 | Cite as

Recommendation using DMF-based fine tuning method

  • Zhiyuan Zhang
  • Yun Liu
  • Guandong Xu
  • Guixun Luo


Recommender Systems (RS) have been comprehensively analyzed in the past decade, Matrix Factorization (MF)-based Collaborative Filtering (CF) method has been proved to be an useful model to improve the performance of recommendation. Factors that inferred from item rating patterns shows the vectors which are useful for MF to characterize both items and users. A recommendation can concluded from good correspondence between item and user factors. A basic MF model starts with an object function, which is consisted of the squared error between original training matrix and predicted matrix as well as the regularization term (regularization parameters). To learn the predicted matrix, recommender systems minimize the squared error which has been regularized. However, two important details have been ignored: (1) the predicted matrix will be more and more accuracy as the iterations carried out, then a fix value of regularization parameters may not be the most suitable choice. (2) the final distribution trend of ratings of predicted matrix is not similar with the original training matrix. Therefore, we propose a Dynamic-MF algorithm and fine tuning method which is quite general to overcome the mentioned detail problems. Some other information, such as social relations, etc, can be easily incorporated into this method (model). The experimental analysis on two large datasets demonstrates that our approaches outperform the basic MF-based method.


Recommender systems Matrix factorization Collaborative filtering Fine tuning Dynamic Social relations 



This work has been supported by the Fundamental Research Funds for the Central Universities 2016YJS028.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zhiyuan Zhang
    • 1
  • Yun Liu
    • 1
  • Guandong Xu
    • 2
  • Guixun Luo
    • 1
  1. 1.School of Communication and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of EducationBeijing Jiaotong UniversityBeijingChina
  2. 2.Advanced Analytics InstituteUniversity of Technology SydneyUltimoAustralia

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