RIGHT: Retrieval-augmented Generation for Mainstream Hashtag
  Recommendation
Run-Ze Fan Yixing Fan Jiangui Chen Jiafeng Guo Ruqing Zhang Xueqi Cheng
Automatic mainstream hashtag recommendation aims to accurately provide users
with concise and popular topical hashtags before publication. Generally,
mainstream hashtag recommendation faces challenges in the comprehensive
difficulty of newly posted tweets in response to new topics, and the accurate
identification of mainstream hashtags beyond semantic correctness. However,
previous retrieval-based methods based on a fixed predefined mainstream hashtag
list excel in producing mainstream hashtags, but fail to understand the
constant flow of up-to-date information. Conversely, generation-based methods
demonstrate a superior ability to comprehend newly posted tweets, but their
capacity is constrained to identifying mainstream hashtags without additional
features. Inspired by the recent success of the retrieval-augmented technique,
in this work, we attempt to adopt this framework to combine the advantages of
both approaches. Meantime, with the help of the generator component, we could
rethink how to further improve the quality of the retriever component at a low
cost. Therefore, we propose RetrIeval-augmented Generative Mainstream HashTag
Recommender (RIGHT), which consists of three components: 1) a retriever seeks
relevant hashtags from the entire tweet-hashtags set; 2) a selector enhances
mainstream identification by introducing global signals; and 3) a generator
incorporates input tweets and selected hashtags to directly generate the
desired hashtags. The experimental results show that our method achieves
significant improvements over state-of-the-art baselines. Moreover, RIGHT can
be easily integrated into large language models, improving the performance of
ChatGPT by more than 10%.