| Title |
Application of List-wise Learning Algorithm in Community Education Course Recommendation |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.3.225 |
| Keywords |
List-wise learning algorithms; Course recommendation; Community education; Trust relationships; Dynamic recommendation |
| Abstract |
Community education is confronted with the challenge of managing information overload. The issue of prompt and precise course recommendations was addressed by proposing a community education course recommendation model based on a listwise learning algorithm for social network recommendation ranking and a dynamic community education course recommendation model based on trust relationships. The list-learning algorithm for social network recommendation ranking had a minimum and maximum cumulative gain of approximately 0.73 and 0.82, respectively. The dynamic recommendation model for community education courses based on trust relationships had a maximum coverage of approximately 0.75, with a root mean square error of 1.25?1.4. The number of dynamic recommendations varied between approximately 2400 and 4100. These methods outperformed other algorithms, suggesting that the model positively affects community education programs and promotes their growth. In summary, the constructed model showed favorable application effects in recommending community education curricula and promoting the development of community education. |