Time-aware Personalized Hashtag Recommendation on Social Media

Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 203–212, Dublin, Ireland, August 23-29 2014. Time-aware Personalized Hashtag Recommendation on Social Media Qi Zhang, Yeyun Gong, Xuyang Sun, Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, P.R.China {qz, 12110240006, 13210240106, xjhuang}@fudan.edu.cn Abstract The task of recommending hashtags for microblogs has been received considerable attention in recent years, and many applications can reap enormous benefits from it. Various approaches have been proposed to study the problem from different aspects. However, the impacts of temporal and personal factors have rarely been considered in the existing methods. In this paper, we propose a novel method that extends the translation based model and incorporates the temporal and personal factors. To overcome the limitation of only being able to recommend hashtags that exist in the training data of the existing methods, the proposed method also incorporates extraction strategies into it. The results of experiments on the data collected from real world microblogging services by crawling demonstrate that the proposed method outperforms state-of-the-art methods that do not consider these aspects. The relative improvement of the proposed method over the method without considering these aspects is around 47.8% in F1-score. 1 Introduction Over the past few years, social media services have become one of the most important communication channels for people. According to the statistic reported by the Pew Research Center’s Internet & American Life Project in Aug 5, 2013, about 72% of adult internet users are also members of at least one social networking site. Hence, microblogs have also been widely used as data sources for public opinion analyses (Bermingham and Smeaton, 2010; Jiang et al., 2011), prediction (Asur and Huberman, 2010; Bollen et al., 2011), reputation management (Pang and Lee, 2008; Otsuka et al., 2012), and many other applications (Sakaki et al., 2010; Becker et al., 2010; Guy et al., 2010; Guy et al., 2013). In addition to the limited number of characters in the content, microblogs also contain a form of metadata tag (hashtag), which is a string of characters preceded by the symbol (#). Hashtags are used to mark the keywords or topics of a microblog. They can occur anywhere in a microblog, at the beginning, middle, or end. Hashtags have been proven to be useful for many applications, including microblog retrieval (Efron, 2010), query expansion (A.Bandyopadhyay et al., 2011), sentiment analysis (Davidov et al., 2010; Wang et al., 2011). However, only a few microblogs contain hashtags provided by their authors. Hence, the task of recommending hashtags for microblogs has become an important research topic and has received considerable attention in recent years. Existing works have studied discriminative models (Ohkura et al., 2006; Heymann et al., 2008) and generative models (Krestel et al., 2009; Blei and Jordan, 2003; Ding et al., 2013) based on textual information from a single microblog. However, from a dataset containing 282.2 million microblogs crawled from Sina Weibo1, we observe that different users …

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