2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud) (2015)
Aug. 24, 2015 to Aug. 26, 2015
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.
Twitter, Tagging, Damping, Web sites, Data mining
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.