中圖分類號(hào)： TP391 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.212165 中文引用格式： 胡一帆，楊小健，秦嶺. 融合用戶行為和評(píng)論文本的圖神經(jīng)網(wǎng)絡(luò)推薦[J].電子技術(shù)應(yīng)用，2022，48(9)：50-54. 英文引用格式： Hu Yifan，Yang Xiaojian，Qin Lin. Graph neural network recommendation combining user behavior and comment text[J]. Application of Electronic Technique，2022，48(9)：50-54.
Graph neural network recommendation combining user behavior and comment text
Hu Yifan，Yang Xiaojian，Qin Lin
College of Computer Science and Technology，Nanjing Tech University，Nanjing 210000，China
Abstract： The existing recommendation algorithm based on graph neural network can make use of graph structure information to improve the recommendation effect, but the main graph structure revolves around a kind of interaction between users and items, but ignores multiple behaviors of users. For example, click, bookmark, share, add to shopping cart, etc., all express different semantics of users, and comment information may affect the next purchase intention of this type of item. To this end, a graph neural network recommendation algorithm based on user behavior and comment information is proposed. The algorithm learns the strength and semantics of user behavior through the graph convolutional network, and then uses the comment text graph to represent the preferences of users and products in the learning reviews, and finally combines them to improve the recommendation effect. According to the experimental results, it is found that the algorithm can improve the recommendation effect to a certain extent.
Key words : recommended system；graph neural network；user behavior
