# Attributes Relevance in Content-Based Music Recommendation System
## 4 Experimental Results
### Impact of a Genre  
The logical and most natural recommendation criterion seems to be the music genre. This is why the influence of the genres on the recommendations was additionally checked. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Strategy** & **User Rating** & **Mark 1** & **Mark 2** & **Mark 3** & **Mark 4** & **Mark 5** & **Avg.** \\  & **No.** & **\%** & **No.** & **\%** & **No.** & **\%** & **No.** & **\%** & **No.** & **\%** & \\ \hline _Random_ & **215** & **71.7** & **44** & **14.7** & 22 & 7.3 & 12 & 4.0 & 7 & 2.3 & 1.51 \\ _Unbalanced_ & 46 & 15.3 & 40 & 13.3 & 56 & 18.7 & 76 & 25.3 & 82 & 27.3 & 3.36 \\ _PrioritizeMFCC_ & 38 & 12.7 & 37 & 12.3 & 46 & 15.3 & **82** & **27.3** & **97** & **32.3** & **3.54** \\ _PrioritizeTempo_ & 44 & 14.7 & 42 & 14.0 & 58 & 19.3 & 74 & 24.7 & 82 & 27.3 & 3.36 \\ _PrioritizeGenre_ & 46 & 15.3 & 41 & 13.7 & **64** & **21.3** & _77_ & 25.7 & 72 & 24.0 & 3.29 \\ \hline \hline \end{tabular} For each rating (value of mark), the result showing which strategy received the most such ratings (given by people checking the recommendation system) is in bold.  
\end{table} 
Table 1: Summary of strategy recommendations and user evaluations. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Compared Strategies** & \(p\)**-Value** & **Difference Statistically Significant** \\ \hline _PrioritizeMFCC_ & \(-\) & _Random_ & \(4.8\times 10^{-61}\) & Yes \\ _PrioritizeTempo_ & \(-\) & _Random_ & \(1.8\times 10^{-53}\) & Yes \\ _PrioritizeGenre_ & \(-\) & _Random_ & \(1.5\times 10^{-52}\) & Yes \\ _Unbalanced_ & \(-\) & _Random_ & \(1.7\times 10^{-54}\) & Yes \\ _PrioritizeMFCC_ & \(-\) & _PrioritizeTempo_ & 0.0038 & Yes \\ _PrioritizeMFCC_ & \(-\) & _PrioritizeGenre_ & \(5.9\times 10^{-5}\) & Yes \\ _PrioritizeMFCC_ & \(-\) & _Unbalanced_ & 0.00059 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical significance tests for different strategies. In this part of the research, the dependence of the strategy's average rating on the input song's genre was tested on the investigated data. Table 3 shows a summary of the developed strategies. The best average value of the mark for each music genre has been bolded. Most of the strategies behave similarly compared to the others, regardless of genre. There is a noticeable drop in performance for the country genre, with all strategies having an average value of a mark of about 2.3. Data analysis for this genre did not reveal an objective relationship between the indicators of individual parameters and lowered ratings. Therefore, this issue will be investigated in future research. The implementation of the recommendations for this genre, through the methods used, achieves clearly worse results, but at the current stage of the research, it cannot be clearly stated that this music genre is more difficult to recommend. Perhaps it is related to the taste of people carrying out the voting in the conducted research. In order to authenticate the difficulty of recommendations for a country music genre, tests with more people, including those who appreciate country music, should be conducted. Table 3 also shows that the _PrioritizeMFCC_ strategy achieved noticeably better results than the others when rating the blues genre, but the difference was insignificant. The musical genre of the songs recommended by each strategy was also checked against the musical genre of the input song. A summary for each strategy can be seen in Table 4. It can be seen that for each of the strategies, there is a tendency to recommend songs of the same genre as the set song. Interestingly, the most genre-like recommendations were generated by the _PrioritizeMFCC_ strategy; out of 300 results, 286 were recommended from the same category. The ratio is virtually identical for the other strategies, with 285 pieces from the same category for the _PrioritizeGenre_ strategy, 283 for _Unbalanced_, and 277 for _PrioritizeTempo_.