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Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion

Author

Listed:
  • Qingyan Zhou

    (School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Hao Li

    (School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Youhua Zhang

    (School of Information and Computer, Anhui Agricultural University, Hefei 230036, China)

  • Junhong Zheng

    (School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

Traditional product evaluation research is to collect data through questionnaires or interviews to optimize product design, but the whole process takes a long time to deploy and cannot fully reflect the market situation. Aiming at this problem, we propose a product evaluation prediction model based on multi-level deep feature fusion of online reviews. It mines product satisfaction from the massive reviews published by users on e-commerce websites, and uses this model to analyze the relationship between design attributes and customer satisfaction, design products based on customer satisfaction. Our proposed model can be divided into the following four parts: First, the DSCNN (Depthwise Separable Convolutions) layer and pooling layer are used to combine extracting shallow features from the primordial data. Secondly, CBAM (Convolutional Block Attention Module) is used to realize the dimension separation of features, enhance the expressive ability of key features in the two dimensions of space and channel, and suppress the influence of redundant information. Thirdly, BiLSTM (Bidirectional Long Short-Term Memory) is used to overcome the complexity and nonlinearity of product evaluation prediction, output the predicted result through the fully connected layer. Finally, using the global optimization capability of the genetic algorithm, the hyperparameter optimization of the model constructed above is carried out. The final forecasting model consists of a series of decision rules that avoid model redundancy and achieve the best forecasting effect. It has been verified that the method proposed in this paper is better than the above-mentioned models in five evaluation indicators such as MSE, MAE, RMSE, MAPE and SMAPE, compared with Support Vector Regression (SVR), DSCNN, BiLSTM and DSCNN-BiLSTM. By predicting customer emotional satisfaction, it can provide accurate decision-making suggestions for enterprises to design new products.

Suggested Citation

  • Qingyan Zhou & Hao Li & Youhua Zhang & Junhong Zheng, 2023. "Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion," Future Internet, MDPI, vol. 15(1), pages 1-16, January.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:1:p:31-:d:1029557
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    References listed on IDEAS

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    1. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
    2. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
    3. Anirut Kantasa-ard & Maroua Nouiri & Abdelghani Bekrar & Abdessamad Ait el cadi & Yves Sallez, 2021. "Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand," International Journal of Production Research, Taylor & Francis Journals, vol. 59(24), pages 7491-7515, December.
    4. Stephen Stajkowski & Deepak Kumar & Pijush Samui & Hossein Bonakdari & Bahram Gharabaghi, 2020. "Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction," Sustainability, MDPI, vol. 12(13), pages 1-18, July.
    5. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
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