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.

[2021-05-05] Printed Article /doc/2020.05.0125/v2