Personalized recommendation with implicit feedback via learning pairwise preferences over item-sets

Weike Pan, Li CHEN, Zhong Ming*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

22 Citations (Scopus)


Preference learning is a fundamental problem in various smart computing applications such as personalized recommendation. Collaborative filtering as a major learning technique aims to make use of users’ feedback, for which some recent works have switched from exploiting explicit feedback to implicit feedback. One fundamental challenge of leveraging implicit feedback is the lack of negative feedback, because there is only some observed relatively “positive” feedback available, making it difficult to learn a prediction model. In this paper, we propose a new and relaxed assumption of pairwise preferences over item-sets, which defines a user’s preference on a set of items (item-set) instead of on a single item only. The relaxed assumption can give us more accurate pairwise preference relationships. With this assumption, we further develop a general algorithm called CoFiSet (collaborative filtering via learning pairwise preferences over item-sets), which contains four variants, CoFiSet(SS), CoFiSet(MOO), CoFiSet(MOS) and CoFiSet(MSO), representing “Set vs. Set,” “Many ‘One vs. One’,” “Many ‘One vs. Set”’ and “Many ‘Set vs. One”’ pairwise comparisons, respectively. Experimental results show that our CoFiSet(MSO) performs better than several state-of-the-art methods on five ranking-oriented evaluation metrics on three real-world data sets.

Original languageEnglish
Pages (from-to)295-318
Number of pages24
JournalKnowledge and Information Systems
Issue number2
Publication statusPublished - 6 Feb 2019

Scopus Subject Areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

User-Defined Keywords

  • Collaborative filtering
  • Implicit feedback
  • Pairwise preferences over item-sets
  • Top-k recommendation


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