Recently, social network websites start to provide third-parity sign-in options via the OAuth 2.0 protocol. For example, users can login Netflix website using their Facebook accounts. By using this service, accounts of the same user are linked together, and so does their information. This fact provides an opportunity of creating more complete profiles of users, leading to improved recommender systems. However, user opinions distributed over different platforms are in different preference structures, such as ratings, rankings, pairwise comparisons, voting, etc. As existing collaborative filtering techniques assume the homogeneity of preference structure, it remains a challenge task of how to learn from different preference structures simultaneously. In this paper, we propose a fuzzy preference relation-based approach to enable collaborative filtering via different preference structures. Experiment results on public datasets demonstrate that our approach can effectively learn from different preference structures, and show strong resistance to noises and biases introduced by cross-structure preference learning.

Original languageEnglish (US)
Title of host publicationKnowledge Science, Engineering and Management - 10th International Conference, KSEM 2017, Proceedings
EditorsZili Zhang, Yong Ge, Zhi Jin, Gang Li, Michael Blumenstein
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319635576
StatePublished - 2017
Event10th International Conference on Knowledge Science, Engineering and Management, KSEM 2017 - Melbourne, Australia
Duration: Aug 19 2017Aug 20 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10412 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th International Conference on Knowledge Science, Engineering and Management, KSEM 2017


  • Data mining
  • Pairwise preference
  • Recommender system

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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