Self Attentive Neural Network for Hashtag Recommendation
A hashtag is a type of metadata tag used on social networks and can help people search for specific topics or content. To capture the interactive information between words and understand the content of microblog posts deeply, this study proposed a neural network model based on a word level self attention mechanism. Given a microblog post, the weight of each word was calculated through a self attention mechanism, and then the representation of a microblog post was obtained through the weighted summation of words. Finally, a multi layer perceptron was adopted on the representation of a microblog post to perform the classification. The effectiveness of the proposed model was verified through experiments of large scale datasets. Results show that: (1) introducing word level self attention mechanism into hashtag recommendation is effective. (2) In comparison with the baseline methods used in previous studies, such as convolutional neural network or long short term memory network, the proposed self attentive neural networks can provide a more accurate representation of a microblog post and significantly improve the F score of hashtag recommendation on the same dataset. This study provides references for the methods and evaluation of short text hashtag recommendations, such as microblogs.