Exploiting Nonlinear Relationships for Top-N Recommender Systems

Zhao Kang, Chong Peng, Ming Yang, Qiang Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


To alleviate the information overload problem, recommendation technology has emerged and flourished. %As a most widely used recommendation technique, collaborative filtering algorithms suffer from data sparseness and cold start problems. Consequently, it is difficult to obtain accurate similarities between users and items and reliable basis of the predictions with these algorithms, leading to sub-optimal recommendation quality. Many state-of-the-art methods usually assume that the data is distributed on a linear hyperplane, which is not the case. The rating data reflect the many-sided interests of users and usually have nonlinear dependencies. In this paper, we map the data into a higher dimensional space and learn the similarity information in this new feature space. Kernel methods are known to be effective for capturing the complex relations in many real world applications. In the first place, a single kernel based algorithm is proposed. It is known that the performance of kernel methods is largely dependent on the choice of kernel. To alleviate such a dependence, we further develop a multiple kernel based algorithm. Experimental results on six real world datasets demonstrate that the proposed algorithms significantly improve the performance of several state-of-the-art recommendation methods.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017
EditorsRuqian Lu, Xindong Wu, Tamer Ozsu, Xindong Wu, Jim Hendler
Number of pages8
ISBN (Electronic)9781538631195
StatePublished - Aug 30 2017
Event2017 IEEE International Conference on Big Knowledge, ICBK 2017 - Hefei, China
Duration: Aug 9 2017Aug 10 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Knowledge, ICBK 2017


Conference2017 IEEE International Conference on Big Knowledge, ICBK 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.


  • Recommender systems
  • kernel method
  • multiple kernel
  • nonlinear relations
  • similarity learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Statistics, Probability and Uncertainty


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