# An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
## 3 Experimental Validation
### Results
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**\# Type**} & \multirow{2}{*}{**Method Description**} & \multicolumn{6}{c}{**Warm System Scenario**} & \multicolumn{6}{c}{**Cold Start Scenario**} \\ \cline{3-14}  & & & **RMSE \(\pm\) s.d.** & **1** & **2** & **3** & **4** & **5** & **6** & **RMSE \(\pm\) s.d.** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1 & CBF & IKNN-LSI-40, k = 40 [this paper] & 1.920 \(\pm\) 0.037 & = * & * & * & * & * & 1.466 \(\pm\) 0.685 & = * & * & * & * \\
2 & OBF & IKNN-RES, p = 2, k = 50 & 1.927 \(\pm\) 0.040 & * & = & * & * & * & 1.583 \(\pm\) 0.733 & * & = & & * \\
3 & OBF & IKNN-LCH, p = 4, k = 80 & 1.927 \(\pm\) 0.040 & * & = & * & * & 1.596 \(\pm\) 0.747 & * & = & * \\
4 & KBF & IKNN-DICE, k = 70 [this paper] & 1.929 \(\pm\) 0.046 & & = & * & 1.601 \(\pm\) 0.786 & & = & * \\
5 & CF & IKNN, k = 40 [14] & 1.940 \(\pm\) 0.039 & * & * & * & = & * & 1.597 \(\pm\) 0.746 & & = & * \\
6 & CF & Sig. Item Asymm. FM, f = 10 [66] & 1.977 \(\pm\) 0.038 & * & * & * & * & 1.763 \(\pm\) 0.848 & * & * & * & * \\
7 & SF & Sig. Comb. Asymm. FM, f = 10 [66] & 1.978 \(\pm\) 0.040 & * & * & * & * & 1.783 \(\pm\) 0.842 & * & * & * & * \\
7 & SF & Sig. User Asymm. FM, f = 10 [66] & 1.982 \(\pm\) 0.039 & * & * & * & * & 1.809 \(\pm\) 0.788 & * & * & * & * \\
7 & User Item Baseline [14] & 2.007 \(\pm\) 0.040 & * & * & * & * & * & 1.753 \(\pm\) 0.851 & * & * & * & * \\
7 & User KNN, k = 80 [14] & 2.021 \(\pm\) 0.041 & * & * & * & * & * & * & 1.753 \(\pm\) 0.851 & * & * & * & * \\
7 & Biased MF, f = 10 [68] & 2.084 \(\pm\) 0.033 & * & * & * & * & * & * & 1.767 \(\pm\) 0.905 & * & * & * & * \\
7 & SVD Plus Plus, f = 10 [69] & 2.120 \(\pm\) 0.043 & * & * & * & * & * & 1.755 \(\pm\) 0.842 & * & * & * & * \\
7 & Slope One [70] & 2.121 \(\pm\) 0.039 & * & * & * & * & * & 1.835 \(\pm\) 0.817 & * & * & * & * \\
7 & Item Average Baseline & 2.164 \(\pm\) 0.045 & * & * & * & * & * & 1.835 \(\pm\) 0.817 & * & * & * & * \\
7 & User Average Baseline & 2.224 \(\pm\) 0.043 & * & * & * & * & * & 1.774 \(\pm\) 0.870 & * & * & * & * \\
7 & MF, f = 10 [15] & 2.254 \(\pm\) 0.044 & * & * & * & * & * & 1.835 \(\pm\) 0.817 & * & * & * & * \\
7 & Global Average Baseline & 2.320 \(\pm\) 0.040 & * & * & * & * & * & 1.835 \(\pm\) 0.817 & * & * & * & * & * \\
7 & Factor Wise MF, f = 10 [67] & 2.787 \(\pm\) 0.148 & * & * & * & * & * & 1.822 \(\pm\) 0.881 & * & * & * & * & * \\  & Random Baseline & 3.857 \(\pm\) 0.057 & * & * & * & * & * & 3.626 \(\pm\) 1.276 & * & * & * & * & * \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison between the best IKNN (item K-nearest neighbors) models and other CF (collaborative filtering) approaches in the Warm System and Cold Start evaluation settings. Method #1 uses transcripts and ratings. Methods #2 and #3 use themes, ontology, and ratings. Method #4 uses themes and ratings. The remaining methods use only ratings with the exception of the random baseline method. Note that the “*” mark is used to indicate that method performance is significantly different at significance level 0.01.