Improving the accuracy of top-N recommendation using a preference model

Jongwuk Lee, Dongwon Lee, Yeon Chang Lee, Won Seok Hwang, Sang Wook Kim

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in recommender systems. We first develop a novel preference model by distinguishing different rating patterns of users, and then apply it to existing collaborative filtering (CF) algorithms. Our preference model, which is inspired by a voting method, is well-suited for representing qualitative user preferences. In particular, it can be easily implemented with less than 100 lines of codes on top of existing CF algorithms such as user-based, item-based, and matrix-factorization-based algorithms. When our preference model is combined to three kinds of CF algorithms, experimental results demonstrate that the preference model can improve the accuracy of all existing CF algorithms such as ATOP and NDCG@25 by 3-24% and 6-98%, respectively.

Original languageEnglish (US)
Pages (from-to)290-304
Number of pages15
JournalInformation Sciences
StatePublished - Jun 20 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems and Management
  • Artificial Intelligence
  • Theoretical Computer Science
  • Control and Systems Engineering
  • Computer Science Applications


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