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- W4313016216 abstract "Personalized hashtag recommendation aims to au-tomatically recommend user-specific hashtags to annotate the posts (e.g., tweets). It is actually an unwieldy procedure because only a small number of posts contain hashtags, which can hinder the quality of recommendation results. Moreover, users always have their own preferences while choosing the hashtag to describe the post contents. Thus, solving label scarcity and user bias both have raised researchers' attention toward hashtag recommendation. The existing methods can be generally divided into two groups: The first group models the relationship between hashtags and posts taking only post contents into account, without considering the user preferences. The second group models the relationship between users and posts assuming that each user has adequate posts tapped with hashtags. In this paper, we propose a Meta-learning based Personalized Hashtag Recommendation (MetaTag) framework to address above mentioned challenges at once. Our method can leverage the prior experience learned from other users and quickly adapt to a new user. In this framework, model is trained in an episodic manner with user-specific historical posts to learn semantic embeddings that can distinguish different hashtags even they have not been seen before. The results of experiments on the data collected from Twitter demonstrated that our method outperforms state-of-the-art approaches by a large margin." @default.
- W4313016216 created "2023-01-05" @default.
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- W4313016216 date "2022-07-18" @default.
- W4313016216 modified "2023-10-16" @default.
- W4313016216 title "Personalized Hashtag Recommendation with User-level Meta-learning" @default.
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- W4313016216 doi "https://doi.org/10.1109/ijcnn55064.2022.9892894" @default.
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