# Review Rating Prediction Based on User Context and Product Context
## 4 Experiments and Evaluations
### Performance Comparison of Different Methods  
To verify the performance of our method, we compared our method and six benchmark methods on four different datasets. The RRP results of nine different methods are shown in Table 2.  
On four different datasets, our approach reduced the MAE and RMSE of the RRP and achieved better performance than the six benchmark methods. Both the RMSE of global methods, such as LR + global and LR + global + UPF, and those of individual methods, such as LR + individual + user and LR + individual + product, are higher than our method. This is because the global methods fail to capture the individuality of each user and product, and the individual methods suffer from data sparseness. Our approach can outperform both the global and individual methods because our method can capture individuality of user and product, and at the same time, exploit the common knowledge shared by different users and products to handle data sparseness problem.  
In addition, our approach performs better than the KNN and MF method. It indicates that our method is a more appropriate way to RRP than KNN and MF. For example, the RMSE of RRP + UC + PC is reduced by 5.71% ((1.0387 \(-\) 0.9794)/1.0387) when compared to MF, which is the best method in baselines in Douban2. The RMSE of RRP + UC + PC is reduced by 7.47% ((0.8042 \(-\) 0.7441)/0.8042) compared to MF, which is the best method in baselines in Yelp 2013.  
The experimental results show that RRP + UC + PC can improve the performance of the RRP. This is because RRP is not only related to the review text content, but also related to user context and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & **Douban1** & **Douban1** & **Douban2** & **Douban2** & **Yelp2014** & **Yelp2013** & **Yelp2013** \\ \hline
**Metric** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline KNN & 1.0659 & 1.4547 & 1.0626 & 1.4271 & 0.7112 & 0.9993 & 0.6987 & 0.9856 \\ MF & 0.8341 & 1.0653 & 0.8056 & 1.0387 & 0.5132 & 0.8146 & 0.4871 & 0.8042 \\ LR + global & 0.8477 & 1.1008 & 0.8277 & 1.0741 & 0.5686 & 0.8985 & 0.5623 & 0.8931 \\ LR + global + UPF & 0.8365 & 1.0681 & 0.8154 & 1.0684 & 0.5305 & 0.8606 & 0.5225 & 0.8334 \\ LR + individual+product & 0.8431 & 1.0909 & 0.8231 & 1.0702 & 0.5589 & 0.8884 & 0.5526 & 0.8736 \\ LR + individual + use & 0.8370 & 1.0703 & 0.8124 & 1.0645 & 0.5383 & 0.8682 & 0.5324 & 0.8533 \\ RRP + PC & 0.8325 & 1.0542 & 0.8170 & 1.0513 & 0.5252 & 0.8523 & 0.5062 & 0.8114 \\ RRP + UC & 0.8216 & 1.0391 & 0.8081 & 1.0282 & 0.5158 & 0.8326 & 0.4961 & 0.8024 \\ RRP + RC + PC & 0.8111 & 0.9899 & 0.7803 & 0.9794 & 0.4841 & 0.7946 & 0.4652 & 0.7441 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean absolute error (MAE) and root mean square error (RMSE) of Six Different Methods in Four Datasets.  
product context. By combining the user context and product context information, the performance of the RRP is improved to some extent.  
From Table 2, we can find a very interesting result. In douban1 and douban2 datasets, the MAE of RRP + UC + PC is only reduced by 4.32% ((0.8477 \(-\) 0.8111)/0.8477) and 5.73% ((0.8277 \(-\) 0.7803)/0.8277) as compared to LR. However, in yelp2014 and yelp2013 datasets, the MAE of RRP + UC + PC is reduced by 14.86% ((0.5686 \(-\) 0.4841)/0.5686) and 17.27% ((0.5623 \(-\) 0.4652)/0.5623) compared to LR. This is because the number of reviews per user and reviews for each product in yelp datasets are much larger than the number of reviews per user and reviews for each product in the douban datasets.