@@ -46,9 +46,9 @@ As it turned out that the \textit{bayesian approach} gave more promising results
...
@@ -46,9 +46,9 @@ As it turned out that the \textit{bayesian approach} gave more promising results
\subsection{Obeservations}
\subsection{Obeservations}
The first observation that emerges from \textit{figure}\ref{fig:bayes_sampling_steps} is that the \textit{increase} in \textit{sampling steps} with a \textit{fixed dimensional embedding} also results in an \textit{improvement} in \textit{RMSE} for all models. Based on this, \textit{figure}\ref{fig:bayes_dimensional_embeddings} also shows that an \textit{increase} in the \textit{dimensional embedding} for \textit{512 sampling steps} also leads to an \textit{improvement} in the \textit{RMSE} for all models. Thus, both the \textit{number of sampling steps} and the size of the \textit{dimensional embedding} are involved in the \textit{RMSE} of \textit{matrix-factorization models} when they are trained using the \textit{bayesian approach}.
The first observation that emerges from \textit{figure}\ref{fig:bayes_sampling_steps} is that the \textit{increase} in \textit{sampling steps} with a \textit{fixed dimensional embedding} also results in an \textit{improvement} in \textit{RMSE} for all models. Based on this, \textit{figure}\ref{fig:bayes_dimensional_embeddings} also shows that an \textit{increase} in the \textit{dimensional embedding} for \textit{512 sampling steps} also leads to an \textit{improvement} in the \textit{RMSE} for all models. Thus, both the \textit{number of sampling steps} and the size of the \textit{dimensional embedding} are involved in the \textit{RMSE} of \textit{matrix-factorization models} when they are trained using the \textit{bayesian approach}.
\subsubsection{Stronger Baselines}
As a second finding, the \textit{RMSE values} of the created models can be taken from \textit{figure}\ref{fig:bayes_dimensional_embeddings}. Several points can be addressed. Firstly, it can be seen that the \textit{individual inclusion} of \textit{implicit knowledge} such as \textit{time} or \textit{user behaviour} leads to a significant \textit{improvement} in the \textit{RMSE}. For example, models like \textit{bayesian timeSVD (0.7587)} and \textit{bayesian SVD++ (0.7563)}, which already use single implicit knowledge, beat the \textit{simple bayesian MF} with an \textit{RMSE} of \textit{0.7633}. In addition, it also shows that the \textit{combination} of \textit{implicit data} further improves the \textit{RMSE}. \textit{Bayesian timeSVD++} achieves an \textit{RMSE} of \textit{0.7523}. Finally, \textit{bayesian timeSVD++ flipped} can achieve an \textit{RMSE} of \textit{0.7485} by adding \textit{more implicit data}.
As a second finding, the \textit{RMSE values} of the created models can be taken from \textit{figure}\ref{fig:bayes_dimensional_embeddings}. Several points can be addressed. Firstly, it can be seen that the \textit{individual inclusion} of \textit{implicit knowledge} such as \textit{time} or \textit{user behaviour} leads to a significant \textit{improvement} in the \textit{RMSE}. For example, models like \textit{bayesian timeSVD (0.7587)} and \textit{bayesian SVD++ (0.7563)}, which already use single implicit knowledge, beat the \textit{simple bayesian MF} with an \textit{RMSE} of \textit{0.7633}. In addition, it also shows that the \textit{combination} of \textit{implicit data} further improves the \textit{RMSE}. \textit{Bayesian timeSVD++} achieves an \textit{RMSE} of \textit{0.7523}. Finally, \textit{bayesian timeSVD++ flipped} can achieve an \textit{RMSE} of \textit{0.7485} by adding \textit{more implicit data}.
This results in the third and most significant observation of the experiment. Firstly, the \textit{simple bayesian MF} with an \textit{RMSE} of \textit{0.7633} already beat the best method \textit{MRMA} with an \textit{RMSE} of \textit{0.7634}. Furthermore, the best method \textit{MRMA} could be surpassed with \textit{bayesian timeSVD++} by 0.0149 with respect to the \textit{RMSE}. Such a result is astonishing, as it took \textit{one year} during the \textit{Netflix-Prize} to reduce the leading \textit{RMSE} from \textit{0.8712 (progress award 2007)} to \textit{0.8616 (progress award 2008)}. Additionally, this result is remarkable as it \textit{challenges} the \textit{last 5 years} of research on the \textit{MovieLens10M-dataset}. Based on the results obtained, the \textit{authors} see the first problem with the \textit{results} achieved on the \textit{MovieLens10M-dataset} as being that they were \textit{compared against} too \textit{weak baselines}.
This results in the third and most significant observation of the experiment. Firstly, the \textit{simple bayesian MF} with an \textit{RMSE} of \textit{0.7633} already beat the best method \textit{MRMA} with an \textit{RMSE} of \textit{0.7634}. Furthermore, the best method \textit{MRMA} could be surpassed with \textit{bayesian timeSVD++} by 0.0149 with respect to the \textit{RMSE}. Such a result is astonishing, as it took \textit{one year} during the \textit{Netflix-Prize} to reduce the leading \textit{RMSE} from \textit{0.8712 (progress award 2007)} to \textit{0.8616 (progress award 2008)}. Additionally, this result is remarkable as it \textit{challenges} the \textit{last 5 years} of research on the \textit{MovieLens10M-dataset}. Based on the results obtained, the \textit{authors} see the first problem with the \textit{results} achieved on the \textit{MovieLens10M-dataset} as being that they were \textit{compared against} too \textit{weak baselines}.
