# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 5 Experiment
### Performance Comparison  
In order to filter out trust relationships with weak trust, the trust threshold \(\delta\) is set, and trust relationships between users with trust strength less than \(\delta\) are filtered out. On the Epinions and LastFM datasets, the confidence thresholds \(\delta\) = 0.95 and \(\delta\) = 0.8, respectively, the number of neighbors \(N\) = 20, and the integration parameters \(\alpha\) = 0.5 and 0.7, respectively, were set. On the NC and WI datasets, the confidence thresholds \(\delta\) = 0.95 and \(\delta\) = 0.8 and the integration parameters \(\alpha\) = 0.9 and 0.8, respectively, were set. All comparison algorithms were performed on four datasets. The experimental results on each dataset are shown in Table 2, Table 3, Table 4 and Table 5, respectively. From Tables 2-5, it can be observed that the performance of Baseline1 algorithms is poor, which is due to the fact that they do not take into account the differences in items and are non-personalized methods. Except for GraphSAGE-CF, PMF outperforms other comparison methods, again validating the effectiveness of the matrix decomposition method. On four datasets, the recommendation accuracy of the proposed collaborative filtering recommendation algorithm based on a graph embedding model is higher than the other comparison algorithms, validating the effectiveness of the proposed algorithm in this paper. On Epinions, LastFM, NC and WI datasets, the improvement of the proposed algorithm in this paper is 2.38%, 2.63%,2.60% 3.14% compared with the optimal results in DeepCoNN, respectively, using RMSE as the reference index. Based on the above rating prediction error, RMSE and MAE, the recommendation methods are compared on the Epinions, LastFM, NC and WI datasets. We have the following main findings:  
* PMF consistently outperforms Baseline1 and Baseline2. Because Baseline1 only uses the average of all scored items scored by the active users as the predicted scores of the target items, Baseline2 uses the weight of similar users' ratings on target items to predict the average of the active users' ratings; At the same time, PMF learns the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Competitor** & _RMSE_ & _MAE_ \\ \hline Baseline1 & 1.478 & 1.208 \\ Baseline2 & 1.403 & 1.124 \\ PMF & 0.923 & 1.062 \\ TrustSVD & 1.174 & 0.904 \\ DeepCoNN & 1.133 & 0.886 \\ GraphSAGE-CF & 1.093 & 0.867 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison on the NC Dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Competitor** & _RMSE_ & _MAE_ \\ \hline Baseline1 & 1.432 & 1.142 \\ Baseline2 & 1.311 & 1.034 \\ PMF & 1.201 & 0.903 \\ TrustSVD & 1.135 & 0.867 \\ DeepCoNN & 1.113 & 0.859 \\ GraphSAGE-CF & 1.091 & 0.841 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison on the Epinions Dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Competitor** & _RMSE_ & _MAE_ \\ \hline Baseline1 & 1.432 & 1.142 \\ Baseline2 & 1.311 & 1.034 \\ PMF & 1.201 & 0.903 \\ TrustSVD & 1.135 & 0.867 \\ DeepCoNN & 1.113 & 0.859 \\ GraphSAGE-CF & 1.091 & 0.841 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison on the LastFM Dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Competitor** & _RMSE_ & _MAE_ \\ \hline Baseline1 & 1.478 & 1.208 \\ Baseline2 & 1.403 & 1.124 \\ PMF & 0.923 & 1.062 \\ TrustSVD & 1.174 & 0.904 \\ DeepCoNN & 1.133 & 0.886 \\ GraphSAGE-CF & 1.093 & 0.867 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison on the WI Dataset. implicit feature vector of users and items from the user-item rating matrix and uses the inner product of the implicit feature vector of users and items;
* TrustSVD obtains a much better performance than PMF. Both methods take the SVD algorithm into model construction. However, TrustSVD uses an advanced SVD++ algorithm and takes direct trust relationship and rating information into the model construction;
* DeepCoNN performances are better than TrustSVD, PMF, Baseline1, and Baseline2.