Text Recommendation Based on Heterogeneous Attention Recurrent Neural Network

• 出版年: Jisuanji gongcheng
• 第一著者: NIU Yaoqiang, MENG Yuyu, NIU Quanfu
• フォーマット: 論文
• 言語: 英語
• 出版事項: Editorial Office of Computer Engineering 2020-10-01
• 主題: short-term text recommendation|data embedding|heterogeneous data|bidirectional gru|attention mechanism
• オンライン･アクセス: https://www.ecice06.com/fileup/1000-3428/PDF/20201006.pdf

To improve the inaccurate text recommendation in the big data environment,this paper merges two kinds of heterogeneous data,text data and relational network,and introduces the encoder-decoder framework.On this basis,a Recurrent Neural Network(RNN) model based on heterogeneous attention is proposed for short-term text recommendation.The sentence-level Distributed Memory Model of Paragraph Vectors(PV-DM) and the representation method for entity relations,TransR,are used to embed text data and relational network into high-dimensional vectors as the input of the model.In the encoding stage,the short-term interests of users are introduced into the recommendation model by using bidirectional GRU,and the attention mechanism is used to connect with the decoder,so that the decoder can dynamically select and linearly combine different parts of the input sequence of the encoder in order to build short-term interests of users.In the decoder stage,the attention output of the encoder,the candidate items,and the representation of current users are taken as inputs.The score of each candidate item is calculated with the bidirectional GRU and the feedforward network layer to obtain the recommendation result.Experimental results show that compared with TF-IDF,ItemKNN and other models,the proposed model significantly improves the recall rate and the average precision of the mean.