Earticle

초록

영어

The methods to resolve Information overload can mainly classify two kinds: Information Retrieval and Information Filtering. Based on the needs of the people, Information Retrieval will search out the related information and technology from the information stored in some way. Information Filtering will choose out user preferred personalized information from the dynamic information flow based on the filtering requests of the people. The paper analyzed micro-blogs users needs and motivation, According to micro-blogs users motivation, effectively, an approach based on tem frequency inverse document frequency (TF-IDF) was proposed. This article constructed personalized recommendation models in on-line social streams based on ties strength, topic relevance and trust dimensions. The experiments on the Sina blogs data showed that the proposed method could reduce the ranks of irrelevant tweets effectively and achieve better performance than several baseline methods based on cosine and hash tags

목차

Abstract
 1. Introduction
 2. Problem and Research Framework
 3. Commendation based on Information Content Characteristic
 4. Information Retrieval Model
 5. The Cold Start Problems
 6. Micro-blog Recommendation Algorithm based on TF-IDF
 7. Experimental Results and Analysis Data
 8. Algorithm Evaluation and Methods Comparison
 9. Conclusion
 Acknowledgement
 References

키워드

저자

• Liuqing Li [ Huanghuai University, Henan, China Henan Agricultural University, Henan, China ]
• Rui Zhang [ Huanghuai University, Henan, China Henan Agricultural University, Henan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605