Earticle

초록

영어

The accuracy of the traditional online recommendation system, much depends on the collaborative filtering recommendation algorithm, however, recommend system aims to attract the interest of consumers and turn visitors into buyers, rather than accurately predict their score. Online recommendation system is the service version of social filtering process. Most previous studies emphasize the accuracy of the collaborative filtering algorithm. However, the effective recommendation system must be credible. It requires that the system logic be transparency and the system be able to provide consumers a new, inexperienced item. Based on the above, this paper proposes to research the quality evaluation of recommendation system from the angle of user’s experience, adding a freshness parameters of Top-N recommend collaborative filtering similarity calculation method, and comparing with the classical recommended algorithm. The experiment result has a certain degree of accuracy and high diversity, which provides basis for establishing the e-commercial recommendation system.

목차

Abstract
 1. Introduction
 2. Recommendation Model Improvement based on Freshness Measurement
  2.1 KNN Measurement Method
  2.2 Predicted Rating Method.
 3. Experimental Analysis and Results
  3.1 Classification of Movie Items in the Dataset
  3.2 Similarity Calculation Method
  3.3 Method of Evaluating Statistical Accuracy
  3.4 The Contrast Experiment
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Xiaofeng Li [ Department of Information Science, Heilongjiang International University, Harbin 150025, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605