## 5 Experimental Results
### Experiment Evaluation  
#### 5.2.1 Effectiveness Evaluation  
The first group of experiments verify the effectiveness of HTPR in term of item clustering and user clustering, the number of nearest neighbors and the number of clusters are determined as well. \begin{table}
\begin{tabular}{l l} \hline \hline
**Data Table** & **Format** \\ \hline Item attribute & \textless{}item id\textgreater{} \textless{}attribute id\textgreater{} \textless{}attribute weight\textgreater{} \\ Item review tag & \textless{}item id\textgreater{} \textless{}tag id\textgreater{} \textless{}tag relevant value\textgreater{} \\ User/item browse & \textless{}user id\textgreater{} \textless{}item id\textgreater{} \textless{}browse tag\textgreater{} \textless{}browse time\textgreater{} \\ User/item wish list & \textless{}user id\textgreater{} \textless{}item id\textgreater{} \textless{} \textless{}add-to-favorite tag\textgreater{} \textless{}add-to-favorite time\textgreater{} \\ User/item purchase & \textless{}user id\textgreater{} \textless{}item id\textgreater{} \textless{} \textless{}purchase tag\textgreater{} \textless{}purchase time\textgreater{} \\ User/item rating & \textless{}user id\textgreater{} \textless{}item id\textgreater{} \textless{} \textless{}rating value\textgreater{} \\ \hline \hline \end{tabular} Note: the data table of item attributes was obtained through keywords extracted from the description of items based on word segmentation, and every word was endowed with weight; the feature tag of item review tag was extracted from the review content by using textual analysis, and the relevant values of tag are given with the range of \((0,1)\).  
\end{table} 
Table 4: Structure of the dataset. Figure 4: Schematic of the training set and test set. (1) Validation test on item clustering. In the first experiment, the effectiveness of the item clustering was examined by measuring the search efficiency of nearest neighbor items. Specifically, the 1628 items in the dataset were clustered under the circumstances of 20 nearest neighbors. We analyzed the impact of different clusters (20, 30, 40) on the search efficiency, the results are shown in Figure 5. Y-coordinate denotes the ratio of searched nearest neighbors of items, and X-coordinate denotes the percentage of items that have been searched. It can be found in Figure 5 that more nearest neighbor items can be found out with less searched items through item clustering. Besides, when the number of clusters was far less than the number of items, the time to compute the similarity between the target item and cluster center was very short, compared to the time used in searching its nearest neighbors. In this case, the greater the number of clusters was, the higher the efficiency of neighbors searching was. However, it may cause a loss to accuracy if the number of clusters is too big. For further verifying the results, rating prediction of the items was made based on ratings of neighbor items and then compared with the data in the test set. The number of clusters varied from 10 to 100 with a step of 10 to compute the average MAE under different circumstances, i.e., the number of nearest neighbors was set to 10, 20, 30, 40, 50 respectively. As shown in Figure 6, the curves of MAE values in different cases as a whole show down first and then up. They decreased obviously when the number of clusters increased from 10 to 40 or 50, and afterwards go up slowly and tend to stabilize. Moreover, it swa noticed that the curve whose nearest neighbors is 30 was lower than other curves, MAE achieved the lowest value when the number of clusters was 40. Therefore, for item clustering, the optimal values of parameters, i.e., the number of clusters and nearest neighbors could be 40 and 30 respectively. Figure 5: Impact of the number of clusters for item clustering. Figure 6: Impact of the number of nearest neighbors on MAE for item clustering. (2) Validation test on user clustering. This experiment was designed to verify the effectiveness of user clustering.