| Title |
Deep Neural Network Entrepreneurial Project Recommendation Model for the Integration of Industry, Education, and Entrepreneurship Needs of Students |
| Authors |
(Tao Long) ; (ZeKun Wang) |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.1.12 |
| Keywords |
Industry-education integration; Entrepreneurial project recommendation; DNN; Matrix decomposition; Word embedding |
| Abstract |
As the size of the entrepreneurship project information platform grows, it is becoming increasingly difficult for student users to find in-demand entrepreneurship projects that integrate industry and education comprehensively and rapidly. The severe information overload leads to poor accuracy of recommendation results. This study addressed these problems based on Deep Neural Networks (DNNs) and Matrix Decomposition Algorithms (MDAs) by combining a Convolutional Neural Network (CNN), word embedding, and one-hot coding techniques. The DNN-MF model was used to extract the entrepreneurial needs and implicit features of students. The DNN-MF model designed for the study was also improved and incorporated with student user features, i.e., the DNN-DNN2 model was constructed. The experiments showed that the Root Mean Square Error (RMSE) of the DNN-MF model was lower than that of the Convolution Matrix Factorization (ConvMF) and Probabilistic Matrix Factorization (PMF) by 0.1190 and 0.1677, respectively. The RMSE of the DNN-DNN2 model was lower than that of the DNN-MF model, and the recommendation accuracy of the study model was 2.35% higher than that of the DNN-DNN1 model, which did not incorporate the student user characteristics. These results showed that the proposed recommendation model for entrepreneurial projects was significantly better than the current popular ones. Moreover, the model could complete the task of recommending entrepreneurial projects faster and more accurately, effectively solving the cold start problem of users and projects, which has certain practical significance. |