PERD: Personalized Emoji Recommendation with Dynamic User Preference
Xuanzhi Zheng, Guoshuai Zhao, Li Zhu, Xueming Qian
Abstract
Emoji recommendation is an important task to help users find appropriate emojis from thousands of candidates based on a short tweet text. Traditional emoji recommendation methods lack personalized recommendation and ignore user historical information in selecting emojis. In this paper, we propose a personalized emoji recommendation with dynamic user preference (PERD) which contains a text encoder and a personalized attention mechanism. In text encoder, a BERT model is contained to learn dense and low-dimensional representations of tweets. In personalized attention, user dynamic preferences are learned according to semantic and sentimental similarity between historical tweets and the tweet which is waiting for emoji recommendation. Informative historical tweets are selected and highlighted. Experiments are carried out on two real-world datasets from Sina Weibo and Twitter. Experimental results validate the superiority of our approach on personalized emoji recommendation.