Cross-Domain Hashtag Recommendation and Story Revelation in Social Media

Real-time micro-blogging services such as Twitter generate billions of micro-posts to share information, daily. However, these posts are inherently noisy and unstructured, hence making them difficult to organize for the purpose of retrieval of relevant information.Hashtags are quickly becoming the standard approach for annotation of various information on social media. However hashtags are not used in a consistent manner and most importantly, are completely optional to use. This makes them unreliable as the sole mechanism for searching for relevant information. In this paper, we investigate mechanisms for consolidating the hashtag space using recommender systems. We propose a cross-domain collaborative filtering recommender system based on matrix factorization (MF) combined with a hashtag similarity graph, to automatically predict relevant hashtags based on an analysis of existing data and the relations among hashtags. Our experiments confirm that our approach outperforms several baselines. Our research using these hashtag graphs for recommendations has further revealed a promising mechanism for revealing stories from the cliques in this graph, paving the road toward story annotation as an extension of hashtag recommendation for tweets.