E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Conversational tagging in twitter
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Mining tweets for tag recommendation on social media
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Dynamical classes of collective attention in twitter
Proceedings of the 21st international conference on World Wide Web
We know what @you #tag: does the dual role affect hashtag adoption?
Proceedings of the 21st international conference on World Wide Web
Using topic models for Twitter hashtag recommendation
Proceedings of the 22nd international conference on World Wide Web companion
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Twitter network is currently overwhelmed by massive amount of tweets generated by its users. To effectively organize and search tweets, users have to depend on appropriate hashtags inserted into tweets. We begin our research on hashtags by first analyzing a Twitter dataset generated by more than 150,000 Singapore users over a three-month period. Among several interesting findings about hashtag usage by this user community, we have found a consistent and significant use of new hashtags on a daily basis. This suggests that most hashtags have very short life span. We further propose a novel hashtag recommendation method based on collaborative filtering and the method recommends hashtags found in the previous month's data. Our method considers both user preferences and tweet content in selecting hashtags to be recommended. Our experiments show that our method yields better performance than recommendation based only on tweet content, even by considering the hashtags adopted by a small number (1 to 3)of users who share similar user preferences.