Improvement of pseudo-voting method in recommender systems

Takayuki Uda, Tetsuo Kinoshita

Research output: Contribution to conferencePaperpeer-review


Collaborative filtering (CF) is widely used as an engine for recommender systems that identify items preferred by users. The importance of such recommender systems has been recognized in the fields of e-commerce, advertisement systems, and so on. However, the challenge of achieving a trade-off between the "coverage of recommendation" and "accuracy of the predicted score" still exists. Hence, a new recommendation system that utilizes the features of items and the pseudo-voting method is proposed to improve both the coverage and the accuracy. Furthermore, the effect of the proposed system is evaluated in this study.

Original languageEnglish
Publication statusPublished - 2007 Jan 1
Event1st Workshop on Information Credibility on the Web, WICOW 2007 - Miyazaki, Japan
Duration: 2007 Jun 192007 Jun 19


Other1st Workshop on Information Credibility on the Web, WICOW 2007


  • Collaborative filtering
  • Feature analysis
  • Pseudo-voting method

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications


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