| 王倩雯,张延华,付琼霄,李萌,李庆.基于双重注意力机制的降噪自编码器推荐算法[J].高技术通讯(中文),2020,30(12):1234~1242 |
| 基于双重注意力机制的降噪自编码器推荐算法 |
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| DOI：10.3772/j.issn.1002-0470.2020.12.004 |
| 中文关键词: 降噪自编码器； 注意力机制； 推荐算法； 辅助信息； 深度学习 |
| 英文关键词: denoising autoencoder, attention mechanism, recommendation algorithm, auxiliary information, deep learning |
| 基金项目: |
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| 摘要点击次数: 2628 |
| 全文下载次数: 1636 |
| 中文摘要: |
| 为了解决传统协同过滤算法的准确度因评分缺失产生的剧烈变化以及冷启动问题，本文提出了一种新的降噪自编码器推荐算法。该方法将注意力机制与辅助信息共同融入降噪自编码器中对评分与交互数据进行处理。首先针对用户交互项目动态分配注意力以学习用户偏好，然后再次通过注意力机制学习隐藏层向量、用户偏好、辅助信息的权重以获得完整评分矩阵。在公开数据集上对该算法进行实验仿真，观察算法性能。结果表明，该算法有效利用了辅助信息且准确度有明显提高。 |
| 英文摘要: |
| In order to solve the problem that the changes in accuracy of the traditional collaborative filtering algorithm due to the lack of ratings and cold start, a new denoising autoencoder algorithm is proposed for recommendation. In this algorithm, attention mechanism and auxiliary information are integrated into the denoising autoencoder model to process the ratings and interactive data. Attention is dynamically allocated to user interaction items to learn user preferences, and then the weights of hidden layer vector, user preferences, and auxiliary information are learned again through the attention mechanism to obtain the complete ratings matrix. The algorithm is simulated on the open data set to observe its performance. The results show that the algorithm uses the auxiliary information effectively and the accuracy is improved obviously. |
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