We can learn your #hashtags: Connecting tweets to explicit topics

In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. From the system's perspective, hashtags play an important role in tweet retrieval, event detection, topic tracking, and advertising, etc. Annotating tweets with the right hashtags can lead to a better user experience. However, two problems remain unsolved during an annotation: (1) Before the user decides to create a new hashtag, is there any way to help her/him find out whether some related hashtags have already been created and widely used? (2) Different users may have different preferences for categorizing tweets. However, few work has been done to study the personalization issue in hashtag recommendation. To address the above problems, we propose a statistical model for personalized hashtag recommendation in this paper. With millions of <;tweet, hashtag> pairs being published everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Two questions are answered in the model: (1) Different from traditional item recommendation data, users and tweets in Twitter have rich auxiliary information like URLs, mentions, locations, social relations, etc. How can we incorporate these features for hashtag recommendation? (2) Different hashtags have different temporal characteristics. Hashtags related to breaking events in the physical world have strong rise-and-fall temporal pattern while some other hashtags remain stable in the system. How can we incorporate hashtag related features to serve for hashtag recommendation? With all the above factors considered, we show that our model successfully outperforms existing methods on real datasets crawled from Twitter.