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.
Zhang et al. REF extended the translation-based models by considering temporal and personal factors.
Time-aware Personalized Hashtag Recommendation on Social Media
COLING
Computer Science