摘要： 传统推荐系统依赖人工进行规则设计和特征提取，对评论文本内容的特征和隐信息的提取能力有限。针对该问题，融合注意力机制并基于深度学习对推荐系统进行改进，提出一种对评论文本深度建模的推荐方法。使用词嵌入模型表达数据集评论中的语义，引入注意力机制对输入内容进行重新赋权，通过并行的卷积神经网络挖掘用户和项目评论数据中的隐含特征，将两组特征耦合输入并采用因子分解机进行评分预测，得到推荐结果。实验结果表明，该方法可有效提高推荐准确率，均方误差较DeepCoNN方法提升2%以上。

关键词: 推荐系统, 特征提取, 注意力机制, 卷积神经网络, 因子分解机

Abstract: The traditional recommendation system relies on manual rule design and feature extraction,resulting in insufficient extraction of the features and implicit information of the review text content.Aiming at this problem,a recommendation method for deep modeling of review text is proposed combining the attention mechanism and the improved recommendation system based on deep learning.The word embedding model is used to express the semantics in dataset comments,the attention mechanism is introduced to re-weight the input content,and the hidden features in the user and project comment data are mined through the parallel convolutional neural network.The two sets of features are coupled and input and scored by a factorization machine to obtain the recommendation results.Experimental results show that the proposed algorithm can efficiently improve the recommendation precision,and the mean square error is improved by more than 2% than that of the DeepCoNN algorithm.

Key words: recommendation system, feature extraction, attention mechanism, Convolution Neural Network(CNN), factorization machine

中图分类号: 

• TP301.6