Earticle

초록

영어

Collaborative filtering recommendation is one of the most effective recommending techniques, which provide customers with suggestions according to their interests. However, neighborhood based collaborative filtering methods confront great challenges of data sparsity and lack of accessorial information in the context of big data. To address these problems, we propose a hybrid model combining tag information and neighborhood based collaborative filtering. A folksonomy network model based on tag information is proposed to analyze the tag relevance between different items. And tag relevance is incorporated into rating prediction of neighborhood based collaborative filtering for improving the recommendation accuracy. Experiments on MovieLens and Netflix datasets are carried out to evaluate the performance of our method. The results show that our method outperforms other methods and can improve recommending quality effectively.

목차

Abstract
 1. Introduction
 2. Background Review
  2.1. Collaborative Filtering
  2.2. Related Works
 3. Hybrid NBM Model Based on Tag
  3.1. Folksonomy Network Model
  3.2. Model Integration
 4. Experiments and Results
  4.1. Experiment Design
  4.2. Experimental Results
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Xiaoyi Deng [ College of Business Administration, Huaqiao University, Quanzhou, 362021, China, Research Center for Applied Statistics and Big Data, Huaqiao University, Xiamen, 361021, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605