#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention

Millions of hashtags are created on social media every day to cross-refer messages concerning similar topics. To help people find the topics they want to discuss, this paper characterizes a user's hashtagging preferences via predicting how likely they will post with a hashtag. It is hypothesized that one's interests in a hashtag are related to what they said before (user history) and the existing posts present the hashtag (hashtag contexts). These factors are married in the deep semantic space built with a pre-trained BERT and a neural topic model via joint training. In this way, user interests learned from the past can be customized to match future hashtags, which is beyond the capability of existing methods assuming unchanged hashtag semantics. Furthermore, we propose a novel personalized topic attention to capture salient contents to personalize hashtag contexts. Experiments on a large-scale Twitter dataset show that our model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. 1 * Jing Li is the corresponding author. † This work was mainly conducted before Ziyan Jiang joined Amazon. 1 Our dataset and code are publicly available in https://github.com/polyusmart/ Personalized-Hashtag-Preferences Sample tweets in H's hashtag contexts. Love thriller and mystery? Check out: URL She is writing the end for a long time. 85-5 star review! Why not visit for Sunday share? Sample tweets in U 's user history. Darpocalypse is now available as an ebook! Read book 2 in the epic living dead series. Zombie Thriller Apocalypse Reader: Zeke is a skilled lover, so easy to fall for. But he has a dark & twisted nature.