基于注意力机制的深度学习推荐算法

针对目前基于评论文本的推荐算法存在文本特征和隐含信息提取能力不足的问题, 提出一种基于注意力机制的深度学习推荐算法. 通过分别构建用户和项目的评论文本表示, 利用双向门控循环单元提取文本的上下文依赖关系以获得文本特征表示, 引入注意力机制, 更准确的获取用户兴趣偏好和项目属性特征. 将生成的用户和项目评论数据的两组隐含特征分别输入全连接层处理, 再合并到同一个向量空间进行评分预测, 得到推荐结果. 在Yelp和Amazon两个公开数据集中进行实验, 结果表明所提出的算法与其他算法相比, 具有更好的推荐性能.

Deep Learning Recommendation Algorithm Based on Attention Mechanism

This study proposes a deep learning recommendation algorithm based on attention mechanism to solve the problem that the current recommendation algorithms based on comment texts have insufficient extraction of text features and implicit information. The comment text representations of users and items are constructed, and the context dependency of texts is extracted by bidirectional gated recurrent units for text feature representations. Moreover, the attention mechanism is introduced to obtain the interest preference of users and the attribute features of items more accurately. The two sets of hidden features of the generated user and item comment data are respectively input into the fully connected layer and then merge into the same vector space for rating prediction. As a result, the recommendation results are obtained. Experiments on two public data sets, Yelp and Amazon, show that the proposed algorithm has better recommendation performance than other algorithms.