# Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
## 7 Results and Discussion
### Recommendation Performance
This, however, is not surprising because the value of relation data depends on the properties of the candidate products. On _CDs and Vinyl_, where most products are music CDs, the CD covers did not reveal much information, and people often express their tastes and preferences in the reviews they wrote. Thus _Mention_ turns out to be the most useful relation. On _Clothing_, however, reviews are not as important as the appearance or picture of the clothes, instead, it is easier to capture item similarities from the items that have been clicked and viewed by the same user. Therefore, adding _Also_view_ relation produces the largest performance improvement for our model on _Clothing_. Figure 3: The NDCG@10 performance of our model (ECFKG, Explainable Collaborative Filtering over Knowledge Graph) and the baseline methods under different embedding sizes. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**CDs and Vinyl**} & \multicolumn{4}{c}{**Clothing**} & \multicolumn{4}{c}{**Cell Phones**} & \multicolumn{4}{c}{**Beauty**} \\ \hline
**Measures(\%)** & **NDCG** & **Recall** & **HR** & **Prec.** & **NDCG** & **Recall** & **HR** & **Prec.** & **NDCG** & **Recall** & **HR** & **Prec.** & **NDCG** & **Recall** & **HR** & **Prec.** \\ \hline
**BPR** & 2.009 & 2.679 & 8.554 & 1.085 & 0.601 & 1.046 & 1.767 & 0.185 & 1.998 & 3.258 & 5.273 & 0.995 & 2.753 & 4.241 & 8.241 & 1.143 \\
**BER-HFT** & 2.661 & 3.570 & 9.926 & 1.268 & 1.067 & 1.819 & 2.872 & 0.297 & 3.515 & 5.307 & 8.125 & 0.860 & 2.934 & 4.459 & 2.826 & 1.132 \\
**VPSP** & 6.031 & 0.845 & 9.203 & 0.328 & 0.560 & 0.968 & 1.557 & 1.067 & 1.379 & 4.850 & 5.002 & 0.507 & 1.901 & 2.786 & 5.961 & 0.902 \\
**TransRec** & 3.372 & 5.283 & 11.956 & 1.837 & 1.245 & 2.078 & 3.116 & 0.312 & 3.361 & 6.279 & 8.725 & 0.962 & 3.218 & 4.853 & 9.867 & 1.285 \\
**DeepCNN** & 4.218 & 6.001 & 13.857 & 1.681 & 1.310 & 2.326 & 3.286 & 0.229 & 3.636 & 6.533 & 9.913 & 0.999 & 3.359 & 5.429 & 9.807 & 1.200 \\
**CCE** & 4.620 & 6.483 & 14.541 & 1.779 & 1.502 & 2.559 & 4.275 & 0.388 & 3.995 & 7.005 & 10.809 & 1.070 & 3.717 & 5.938 & 11.043 & 1.371 \\
**JRL** & 5.378 & 7.545 & 16.774 & 2.085 & 1.735 & 2.999 & 4.634 & 0.442 & 4.464 & 7.510 & 10.940 & 1.096 & 4.396 & 6.949 & 12.776 & \(\uparrow\) & \(\downarrow\) & \(\uparrow\) & \(\downarrow\) & \(\uparrow\) & \(\uparrow\) & \(\downarrow\) \\
**Our model** & **5.563** & **7.949** & **17.556** & **2.192** & **3.091** & **5.466** & **7.972** & **0.763** & **5.370** & **9.498** & **13.455** & **1.325** & **6.399** & **10.411** & **17.498** & **1.996** \\
**Improvement** & 3.44 & 5.35 & 4.66 & 5.13 & 78.16 & 82.87 & 72.03 & 72.62 & 23.05 & 26.47 & 22.99 & 20.89 & 45.56 & 49.82 & 36.96 & 28.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the baselines and our model on top-10 recommendation. All the values in the table are percentage numbers with “\(\%\) omitted, and all differences are significant at \(p<0.05\). The starred numbers (\(\uparrow\)) indicate the best baseline performances, and the bolded numbers indicate the best performance of each column. The last line shows the percentage improvement of our model against the best baseline (i.e., JRL), which are significant at \(p<0.001\). \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline
**Relations** & \multicolumn{4}{c}{**CDs and Vinyl**} & \multicolumn{4}{c}{**Clothing**} & \multicolumn{4}{c}{**Cell Phones**} & \multicolumn{4}{c}{**Beauty**} \\ \hline
**Measures(\%)** & **NDCG** & **Recall** & **HT** & **Prec** & **NDCG** & **Recall** & **HT** & **Prec** & **NDCG** & **Recall** & **HT** & **Prec** & **NDCG** & **Recall** & **HT** & **Prec** \\ \hline
**_Purchase only_** & 1.725 & 2.319 & 7.052 & 0.818 & 0.974 & 1.665 & 2.651 & 0.254 & 2.581 & 4.526 & 6.611 & 0.649 & 2.482 & 3.834 & 7.432 & 0.948 \\ _+Also\(\_\)rieu_ & 1.722 & 2.336 & 6.967 &  
Overall, it is difficult to find a product knowledge that is universally useful for recommending products from all categories.