多头注意力评论量化的聚类优化推荐算法

Clustering optimization recommendation algorithm for multi-head attention comment quantization

为了解决推荐算法中无法挖掘用户深层兴趣偏好,从而导致提取准确度低下,以及相似用户聚类准确率低下时间复杂度高等问题,提出评论量化模型优化差分进化的聚类优化推荐算法(MT-QRPD)。首先利用BiGRU网络的特征时序性与CNN的强局部特征有效性联合提取评论深度特征,并利用多头注意力机制的多维语义特征筛选对评论进行深度语义特征挖掘;然后经过多层感知机非线性转换进行多特征融合完成准确量化;最后使用PCA对差分进化变异选择进行优化完成相似用户聚类优化操作,寻找相似用户完成项目推荐。通过多项实验分析表明,所提推荐算法在量化评分准确度、时间复杂度以及推荐性能上都有较好的提升。

In order to solve the problem that users’deep interests and preferences cannot excavate in the recommendation algorithm,which leads to low extraction accuracy and high time complexity of similar users’clustering accuracy,this paper proposed a clustering optimization recommendation algorithm(MT-QRPD)based on the evaluation quantization model to optimize differential evolution.Firstly,it used the feature timing of BiGRU network and the strong local feature effectiveness of CNN network to extract the comment depth features,and used the multi-dimensional semantic feature screening of multi-head attention mechanism to mine the comment depth semantic features.Then,through the nonlinear transformation of the multi-layer perceptron carried out the multi-feature fusion to achieve accurate quantification.Finally,the algorithm optimized the clustering of similar users with PCA,and completed the project recommendation.The result of experiments shows that the proposed algorithm can improve the accuracy,time complexity and recommendation performance.