# Movie Recommendation through Multiple Bias Analysis +
## 5 Conclusions  
The performance limit of recommendation systems follows analysis algorithm and data problems. The analysis algorithm problem is \"What algorithm fits the given data?\"  
Figure 5: Comparison of RMSE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**RMSE**} & \multicolumn{2}{c}{**Hybrid Parameters**} & \multicolumn{2}{c}{**Factorization**} \\ \cline{3-5}  & & \(Iter\) & \(\lambda\) & **Model Parameters** \\ \hline MF & 1.006741 & N/A & N/A & \\ HybridMF(RF) & 1.023260 & 200 & 0.001 & \(\lambda=0.01,\gamma=0.005\), \\ HybridMF(ARF) & 1.015874 & 200 & 0.001 & \(f=10,Iter=40\) \\ HybridMF(LRF) & 0.996805 & 150 & 0.001 & \\ \hline SVD++ & 0.968985 & N/A & N/A & \\ HybridSVD++(RF) & 1.009454 & 200 & 0.001 & MF: \(\lambda=0.01,\gamma=0.005\), \\ HybridSVD++(ARF) & 1.001016 & 250 & 0.001 & \(f=10,Iter=40\) \\ HybridSVD++(LRF) & 0.959599 & 150 & 0.001 & Bias: \(\lambda=0.5,\gamma=0.35\), \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of results (Hybrid model). If \"an arbitrary dataset \(\chi\) showed 91% performance when classified by support vector machine (SVM) and 85% when classified by \(k\)-nearest neighbor(\(k\)-NN)\", SVM can be said to be good. Here, SVM and \(k\)-NN become the analysis viewpoints, and a hybrid model that adds viewpoints can be used to improve the performance. The data problem is a missing-value problem. Because rating is the result of decision-making regarding a user's subjectivity, preference, situation, and environment, consistency cannot be guaranteed. However, rating data are uncertain correct answer sheets, because information about the process whereby the user makes the decision is lacking. Therefore, the accuracy of recommendation systems should have converged on the current level because a limit to understanding decision-making exists. When considering the process for a user to select a movie, actors, director, and genres can be important variables. If the user likes a certain genre, he/she is highly likely to prefer an actor thta is close to the action or to be a fan of the actor. For example, [PERSON], [PERSON], and [PERSON] belong to action. Furthermore, for a director that is represented by a certain genre, actors related to the director can be considered. MBA's research motive is to observe this pattern through the rating frequency and the degree of tendency of ratings, and this was defined as a bias analysis. MBA is a model to analyze the foregoing, and through experiments, BBP accuracy, which is the basis of MBA, has been improved, and it was shown the result of Table 12 that the hybrid model outperforms MF and SVD++. Based on this result, it can be argued that the bias is reflected in users' decision-making for movies. MBA was designed to learn the feature weight \(\omega\) that was used in the user's GF and PF. When the \(\omega\) values used for GF and PF were observed, it was identified that they could be used as the user's unique characteristic. Because HybridSVD++ (LRF) outperformed SVD++, which is limited to the foregoing, it can be a clue to explain the user's decision-making process using the \(\omega\) values of GF and PF. Furthermore, using the \(\omega\) values of GF and PF, it is possible to design a recommendation system that describes the reason for recommendation simultaneously with the recommendation. For example, when MBA has recommended the movie \"Iron Man\", how crucial \"[PERSON]\" and \"Genre-SF\" were can be computed numerically. Reflecting this concept, it is possible to perform a data description that explains the reason for recommendation simultaneously with the recommendation. Thus far, bias has been considered at the graph-axis movement level, such as \(y=ax+b\) and negative variables. Although using bias requires more observation and verification, given the influence of bias that is involved in decision-making in music, food, movies, books, news, etc., bias is an element that is more important than the graph-axis movement.