The Community for Technology Leaders
2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud) (2015)
Rome, Italy
Aug. 24, 2015 to Aug. 26, 2015
ISBN: 978-1-4673-8102-4
pp: 698-702
ABSTRACT
Hash tag recommendation is the problem of finding interesting hash tags for a user, which are not easily found via Twitter search. Searching a hash tag simply shows a list of tweets, each contains the query hash tag string. To find even more relevant hash tags, we propose to use a graph-based approach to find similar hash tags by using the social network graph around hash tags. We start by using a heterogeneous social graph that contains users, tweets, and hash tags, then we summarize the graph to a hash tag graph that shows the similarity between different hash tags. Finally, we rank the vertices in respect to a query hash tag using a random walk with restart and a content similarity measure. The experimental work demonstrates the effectiveness of our approach compared to baselines.
INDEX TERMS
Twitter, Tagging, Damping, Web sites, Data mining
CITATION

M. Al-Dhelaan and H. Alhawasi, "Graph Summarization for Hashtag Recommendation," 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud)(FICLOUD), Rome, Italy, 2015, pp. 698-702.
doi:10.1109/FiCloud.2015.61
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