# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 4 Experiments
### Experimental Results and Analysis
#### 4.2.1 Result 1: Performance Comparison  
To explore the performance of IFDNAMF, we compared the performance of the proposed IFDNAMF model with the four models: GMF, GMF+MLP, concat, sum pooling, and GMF. The five models were trained and tested on MovieLens and Book-crossing, respectively. Table 1 shows the performance of these models's HR@10 and NDCG@10. It is not hard to see from Table 1 that on both datasets, the performance of the two indicators HR@10 and NDCG@10 is: IFDNAMF > sum pooling > concat > GMF+MLP > GMF. The performance of our proposed IFDNAMF model is superior to the baselines on both datasets. Compared with the sum pooling model, the two indicators of the IFDNAMF model on the Book-crossing have increased by 0.31% and 0.17%, while the two indicators of the IFNDAMF model on MovieLens have improved by 0.51% and 0.33%. Compared with the GMF model, the HR@10 and NDCG@10 of IFDNAMF have increased by 3.6% and 2.67% on MovieLens, while 4.66% and 4.9% on the Book-crossing. Among these models, GMF performs the worst, followed by the GMF+MLP model. This may be caused by the fact that the GMF and GMF+MLP only model the user ID feature and item ID feature, which \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**MovieLens**} & \multicolumn{2}{c}{**Book-Crossing**} \\ \hline
**Baseline** & **HR@10** & **NDCG@10** & **HR@10** & **NDCG@10** \\
**GMF** & 0.7141 & 0.4357 & 0.6518 & 0.3739 \\
**GMF+MLP** & 0.7318 & 0.4502 & 0.6831 & 0.4150 \\
**concat** & 0.7369 & 0.4528 & 0.6878 & 0.4157 \\
**Sum pooling** & 0.7450 & 0.4600 & 0.6953 & 0.4212 \\
**IFDNAMF** & 0.7501 & 0.4633 & 0.6984 & 0.4229 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of the five models on MovieLens and Book-crossing. leads to the loss of the partial user preference and item representation, that is, they could only represent the partly information of user preference and item representation. The other three models have fused features of other attribute information of users and items, as well as the user ID feature and the item ID feature. Through the auxiliary information, they could model user preference and item representation more comprehensively and accurately from multiple aspects, thereby the performance of recommendation systems gets improved. This also demonstrates the effectiveness of the way that introduces auxiliary information into the model to more comprehensively model user preference and item representation. Furthermore, compared with the sum pooling model and IFDNAMF model, the performance improvement of the concat model is relatively worse. This indicates that, when fusing attribute information, the simple concatenating operation on the segmented attribute feature vectors through the fully connected network is not effective to capture the interactive relationships between the features, which will lead to the limitation on the performance of the model, while through the element-wise product, the cross between different feature domains is completed in a targeted manner, and the cross information between features will be captured, which enhances the model's ability to represent different data patterns. Meanwhile, Table 1 shows that the IFDNAMF model performs better than the sum pooling model on both datasets. This is due to that different cross-features have different degrees of importance in the rating prediction task, compared with the sum pooling model, the IFDNAMF model can assign different weights to different cross-features through the attention mechanism, thereby effectively distinguishing the correlation of different features on the result. This indicates that it is feasible and effective to introduce the attention mechanism to the model. In addition, the HR@10 and NDCG@10 of the GMF+MLP perform better than the GMF model on both datasets. This is caused by the fact that the GMF is a shallow model, although it can well model the non-linear second-order interaction between users and items, it cannot effectively model the high-order interaction between users and items.