Earticle

초록

영어

To overcome the problem of too much sparse by scoring matrix, the paper proposes from the part of user interests the collaborative filtering recommendation algorithm based on such interest. In the e-commerce websites, recommendation items have various appearance, function and category attributes. Items which hold similar characteristics generally gain approximate scoring values. The aforesaid method extracts useful information for improvement from user interests. Users’ scorings about different items imply their modes of interest. Interest association exists amongst items that were evaluated by the same user. Since individual interest shifts, the intensity of such correlation will gradually change along with days. By building interest intensity model with time decay, and discovering interest correlation among different items through that model, the proposed algorithm can predict scoring matrix and fill it, which is helpful to alleviate problems with sparse caused by user-item scoring matrix.

목차

Abstract
 1. Introduction
 2. User Interests
 3. Collaborative Filtering Recommendation Algorithm Based on User Interests
  3.1. Interest Association Analysis in CFBUI
  3.2. Interest Association Degree Analysis with Time Decay in CFBUI
  3.3. Comprehensive Similarity of CFBUI
  3.4. Workflow of the Proposed Method
 4. Experiment Design and Discussion
  4.1. Evaluation Standard
  4.2. Dataset
  4.3. Experimental Strategy
  4.4. Test Environment
  4.5. Analysis of Parameters
  4.6. Analysis of Results
 5. Conclusion
 References

키워드

저자

• Zuping Liu [ Sichuan Vocational and Technical College, Suining 629000, 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