# Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
## 4 Experiments
### Experimental Results and Analysis  
Experiments were conducted on the DBLP-v11 dataset with the following parameters: the vector dimension D = 512, the number of neighbors extracted per layer in the knowledge graph M = 16, and the number of aggregation layers in the knowledge graph L = 2. The experimental results are presented in Table 3 below. The results indicate that the model presented in this paper outperforms other baseline methods based on the selected evaluation metrics. The CF method is susceptible to the adverse effects of data sparsity and only considers the similarity between users and papers without utilizing richer feature information, leading to very poor recommendation performance. The NCF model, which is based on CF, shows a significant improvement, likely due to the introduction of neural networks to learn the interaction features between users and papers, thereby mitigating the impact of data sparsity. However, the NCF \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **HR** & **NDCG** & **Prec@10** & **Rec@10** \\ \hline CF & 0.386 & 0.245 & 0.197 & 0.238 \\ NCF & 0.655 & 0.390 & 0.355 & 0.372 \\ DeepCoNN & 0.714 & 0.431 & 0.423 & 0.455 \\ DKN & 0.757 & 0.453 & 0.484 & 0.522 \\ RGCN & 0.769 & 0.459 & 0.512 & 0.526 \\ EGAT & 0.786 & 0.477 & 0.525 & 0.565 \\ PNA & 0.791 & 0.481 & 0.534 & 0.551 \\ MFPRKG & 0.812 & 0.497 & 0.553 & 0.589 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparative experimental results. model only considers implicit feedback information and does not fully utilize the textual information of papers. In contrast, the DeepCoNN model, which takes into account the domain and language features of users and papers, achieved a notable improvement in recommendation accuracy. The recommendation methods that incorporate knowledge graphs, such as DKN, RGCN, PNA, and EGAT, consider the connections between various entities and integrate higher-order information into the recommendation process, resulting in a considerable enhancement in recommendation performance. DKN leverages semantic information from knowledge graphs to deeply understand user needs and paper content, while RGCN further improves the accuracy of recommendations by dealing with complex interactions in multi-relational heterogeneous graphs. The PNA model, by integrating multiple aggregators and scalers, significantly enhances the capture and utilization of graph-structured data, demonstrating excellent performance in recommendation tasks. The EGAT model, by integrating both node and edge features, effectively addresses the impact of edge features on task results, enhancing the personalization of recommendations. The MFPRKG model achieves better performance than other baseline methods in evaluation metrics. The MFPRKG model comprehensively utilizes various information sources and advanced recommendation algorithms to more accurately capture users' implicit and explicit feedback, the deep semantic information of paper content, and the interconnections between various entities, ultimately resulting in higher accuracy and personalized recommendation outcomes. Firstly, experiments were conducted with different values for the vector dimension D between users and papers, specifically selecting D = 128, D = 256, and D = 512. As the value of D increased, the performance of the recommendation model gradually improved. A lower dimension may lead to information loss and insufficient model expressiveness, while a higher dimension might increase the model's complexity. After considering the trade-offs, D was set to 512 to achieve better recommendation results. Next, the impact of the number of knowledge graph aggregation layers L on model performance was studied, with considerations of L = 1, L = 2, and L = 3. The results showed that the model performed best when L was set to 2. A lower number of layers might limit the model's consideration of multi-layered information interactions, while a higher number of layers, although increasing the model's complexity, could lead to overfitting.