# Social Recommendation Based on Quantified Trust and User's Primary Preference Space
## 6 Experiments and Validations
### Experimental Results
#### 6.3.2 Experimental Results of the TPSR Framework  
Global experiments on all users. This experiment is used to verify the recommendation effect of the TPSR framework on all users' data. There are two goals in the recommendation area, including obtaining the predicted ratings of items and recommending the item list. Hence, in this part, we also conduct two experiments: (a) the experiment to verify rating prediction accuracy using _RMSE_ as the indicator of accuracy, and (b) the experiment to measure item recommendation list quality, using _accuracy, recall_ and _F1_ value as indicators of the quality of the list. The TPSR framework is compared with eight other methods, and the results are shown in Tables 4 and 5. As shown in Table 4, compared with traditional collaborative filtering recommendation methods of CF and PMF (only interactive information, not trust information), the _RMSE_ of the other seven social recommendation algorithms are reduced, which indicates that \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **CF** & **PMF** & **SoRec** & **TrustPMF** & **TPMF** & **SEEPS** & **DANSER** & **TrustEV** & **TPSR** \\ \hline FilmTrust & 0.9600 & 0.9040 & 0.8810 & 0.8783 & 0.8504 & 0.8661 & 1.0294 & 0.8405 & **0.8380** \\ Epinions & 1.4107 & 1.4052 & 1.2712 & 1.2604 & 1.2496 & 1.0806 & 1.0268 & 1.1319 & **1.2275** \\ Douban & 0.8314 & 0.8269 & 0.7670 & 0.7737 & 0.7761 & 0.7682 & - & 0.7674 & **0.7549** \\ Ciao & 1.2297 & 1.2164 & 1.0302 & 0.9910 & 1.0522 & 0.9633 & - & 1.2491 & **0.9572** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Rating Prediction Accuracy RMSE Experiment. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Metrics** & **CF** & **PMF** & **SoRec** & **TrustPMF** & **TPMF** & **SEEPS** & **TPSR** \\ \hline FilmTrust & Precision & 0.7847 & 0.7869 & 0.8021 & 0.8047 & 0.8104 & 0.8195 & **0.8296** \\ FilmTrust & Recall & 0.8218 & 0.8426 & 0.8616 & 0.8834 & 0.8914 & 0.8897 & **0.9040** \\  & F1 & 0.8028 & 0.8138 & 0.8307 & 0.8422 & 0.8490 & 0.8531 & **0.8553** \\  & Precision & 0.7827 & 0.7983 & 0.7946 & 0.8185 & 0.8204 & 0.8312 &  
using trust information for social recommendation can effectively reduce the error of the rating prediction. Among the seven social recommendation methods using trust data, the proposed TPSR framework has the lowest _RMSE_ on FilmTrust, Douban and Ciao datasets. Only in the Epinions dataset is the _RMSE_ of TPSR slightly higher than that of the SREPS, DANSER and TrustEV algorithms. Although the DANSER algorithm has achieved the best result in the Epinions dataset, the result in the small dataset from FilmTrust is not ideal and is far higher than TPSR. When we ran the DANSER algorithm on the Douban and Ciao datasets, we found that the efficiency of the algorithm was very low. We have estimated that it may take several months to train the model; however, TPSR only takes approximately twenty minutes. Therefore, the results of these two sets of experiments are not shown in Table 4. As shown in Table 2, the density of scoring data in the Epinions dataset is much lower than that of the other three datasets, while graph-embedding methods and neural network methods have inherent advantages for sparse data mining at the cost of time complexity. In short, in terms of prediction accuracy and time complexity, compared with the existing recommendation algorithm, the proposed TPSR framework has certain advantages. In the recommendation list experiment, items with ratings greater than 4 are considered to be items that the user really loves. As shown in Table 5, compared with other methods, TPSR has highest value in terms of _accuracy, recall_ and _F1_ value. The results show that TPSR can effectively improve the quality of the recommendation list.