Earticle

초록

영어

Traditional collaborative filtering methods just use user-item rating matrix to generate recommendations, and lead to difficult to computer the similarity because of the data sparsity. We propose a hybrid collaborative filtering algorithm combining the rating matrix and item attributes. First, we design a user similarity measurement method by computing the user’s preference to different item attributes, this approach is consistent with the true relationship between users, and also can effectively alleviate the issue of rating matrix sparse. Then, when computing the similarity of two users, we combine the Pearson correlation and the items attribute preference similarity, with a weighting coefficient “w” to balance the importance of two parts. Experiments show that this algorithm effectively solves the problem of data sparsity, and outperforms better when the sparsity is more serious, compared to the traditional CF algorithms.

목차

Abstract
 1. Introduction
 2. Problem and Existing Solutions
 3. Hybrid Collaborative Filtering based on user Rating and Item Attribute Preference
  3.1. Measure of User Similarity based on Item Attribute Preference
  3.2. Workflow of the Algorithm
 4. Experiment Design and Analysis
  4.1 Experimental Data
  4.2 Experimental Results and Discussion
 5. Conclusion
 References

키워드

저자

• Song Li [ Institute of Engineering, Mudanjiang Normal University Mudanjiang 157011, 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