DocumentCode :
3000390
Title :
Applying hidden topics in ranking social update streams on Twitter
Author :
Thi-Tuoi Nguyen ; Tri-Thanh Nguyen ; Quang-Thuy Ha
Author_Institution :
KTLab, Univ. of Eng. & Technol., Hanoi, Vietnam
fYear :
2013
fDate :
10-13 Nov. 2013
Firstpage :
180
Lastpage :
185
Abstract :
As the number of users using Twitter1 increases, an user may have a lot of friends whose tweet (posting) list (also called as “social update stream” [5, 8, 18]) may overwhelm his/her homepage. This can lead to the situation where important tweets (i.e. the tweets the user is interested in) are pushed down on the list, thus, it takes time to find them. Social update stream ranking is a possible solution that puts important tweets on the top of the page, so that the user can easily read it. In this paper, we propose to apply hidden topics [1, 15, 20] in the Combined Regression Ranking algorithm [2] to rank social update streams. The proposed system works like a content based recommendation system. The experimental results show a significant improvement proving that our proposal is a suitable direction.
Keywords :
recommender systems; regression analysis; social networking (online); Twitter; combined regression ranking algorithm; content based recommendation system; posting list; social update stream ranking; social update streams; tweet list; Data models; Educational institutions; Optimization; Semantics; Training; Twitter; Vectors; Twitter; hidden topic; laten drichlet allocation; social network; social update stream ranking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2013 IEEE RIVF International Conference on
Conference_Location :
Hanoi
Print_ISBN :
978-1-4799-1349-7
Type :
conf
DOI :
10.1109/RIVF.2013.6719890
Filename :
6719890
Link To Document :
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