Design of packaging style recommendation system based on user behavior analysis and emotional feature extraction
Yuan Shen, Renwei Li, Xin Cao, Majid Hussain · PeerJ Computer Science · 2026
With the rapid growth of e-commerce, consumer demand for personalized packaging solutions has grown significantly. To address this issue, this article constructs an intelligent recommendation model that combines user behavior data and sentiment analysis techniques. Firstly, a user behavior preference model is established by collecting potential preferences and behavioral characteristics of users, in order to explore their network behavior characteristics. Next, the Bidirectional Encoder Representations from Transformers (BERT) word vector is used to represent the comment text, and a bidirectional recurrent neural network is used to quantify the emotional information in the comment. Based on the emotional rating, the rating matrix is updated to map the shallow features of users and resources. Subsequently, by combining convolutional neural networks and self attention mechanisms, deep features of users and resources are extracted from comment texts, and shallow and deep features are fused through multi-layer neural networks to model the nonlinear interaction between users and resources and predict the rating values of recommended resources. In addition, this article proposes a hierarchical attention enhanced recommendation model incorporating User behavior and sentiment analysis (HAER-UBSA), which obtains feature information from comment texts through attention mechanisms and models comment level embedded representations of users and items. The experimental results show that compared with other baseline models, the mean absolute error (MAE) and root mean squared error (RMSE) indicators of our model have improved by 10.34% and 10.00%, respectively.