Earticle

초록

영어

Traditional methods UBCF have limitations of poor recommendation quality and problems of data sparsity. To alleviate these problems, a novel collaborative filtering algorithm is designed, which firstly get the users’ ratings and time intervals for each attribute from the users’ ratings for items, then produce two methods to calculate the similarity between users, introduce a weighting parameters to control the weight between the two similarity methods in order to get a fusion similarity between two users. The results show that this method is able to improve the accuracy of predicted values, resulting in improving recommendation quality of the collaborative filtering recommendation algorithm.

목차

Abstract
 1. Introduction
 2. Defects to User-based Collaborative Filtering
 3. Fusion of Rating Similarity and Time Interval on the Item Attributes
  3.1. Similarity Computation
  3.2. The Matrix of User-item, Item-attribute and Time-interval
  3.3. Similarity of User-attribute
  3.4. Similarity Fusion
  3.5. Predict Ratings
 4. Experiment and Result Analysis
  4.1. Datasets
  4.2. The Metric
  4.3. Comparing with the Traditional UBCF
 5. Conclusion
 References

키워드

저자

• Xiao-hui Cheng [ College of Information Science and Engineering, Guilin University of Technology, Guilin Guangxi, China ]
• Yu Wu [ College of Information Science and Engineering, Guilin University of Technology, Guilin Guangxi, China ]
• Yun Deng [ College of Information Science and Engineering, Guilin University of Technology, Guilin Guangxi, China ] corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605