An Effective Preference Model to Improve Top-N Recommendation 


Vol. 44,  No. 6, pp. 621-627, Jun.  2017
10.5626/JOK.2017.44.6.621


PDF

  Abstract

Collaborative filtering is a technique that effectively recommends unrated items for users. Collaborative filtering is based on the similarity of the items evaluated by users. The existing top-N recommendation methods are based on pair-wise and list-wise preference models. However, these methods do not effectively represent the relative preference of items that are evaluated by users, and can not reflect the importance of each item. In this paper, we propose a new method to represent user"s latent preference by combining an existing preference model and the notion of inverse user frequency. The proposed method improves the accuracy of existing methods by up to two times.


  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

J. Lee and J. Lee, "An Effective Preference Model to Improve Top-N Recommendation," Journal of KIISE, JOK, vol. 44, no. 6, pp. 621-627, 2017. DOI: 10.5626/JOK.2017.44.6.621.


[ACM Style]

Jaewoong Lee and Jongwuk Lee. 2017. An Effective Preference Model to Improve Top-N Recommendation. Journal of KIISE, JOK, 44, 6, (2017), 621-627. DOI: 10.5626/JOK.2017.44.6.621.


[KCI Style]

이재웅, 이종욱, "상위 N개 항목의 추천 정확도 향상을 위한 효과적인 선호도 표현방법," 한국정보과학회 논문지, 제44권, 제6호, 621~627쪽, 2017. DOI: 10.5626/JOK.2017.44.6.621.


[Endnote/Zotero/Mendeley (RIS)]  Download


[BibTeX]  Download



Search




Journal of KIISE

  • ISSN : 2383-630X(Print)
  • ISSN : 2383-6296(Electronic)
  • KCI Accredited Journal

Editorial Office

  • Tel. +82-2-588-9240
  • Fax. +82-2-521-1352
  • E-mail. chwoo@kiise.or.kr