% \begin{figure}
%     \centering
%     \scalebox{0.8}{
%     \includegraphics[width=\textwidth]{figures/case_study/case_study_116.png}
%     }
%     \caption{A case study on interpretability.}
%     \label{fig:case_study}
% \end{figure}

% \begin{wrapfigure}{r}{0.5\textwidth}
%     \centering
%     \includegraphics[width=0.5\textwidth]{figures/case_study/case_study_116.png}
%     \small
%     \caption{Visualization of predicted words and items (each has image and title) of text and rating decoders. Factor alignment  is based on probability in Figure ~\ref{fig:case_study_116_0.1}. Words in item titles are boldfaced.\looseness=-1}
%     % on the right while some other words appear in item descriptions, which we skip here to save space.\looseness=-1}
%     \label{fig:case_study}
% \end{wrapfigure}

% \begin{figure}[!ht]
%     \centering
%     \scalebox{0.75}{
%     \includegraphics[width=0.5\textwidth]{figures/case_study/676_case_study.png}
%     }
%     \small
%     \caption{Illustration of aligning interest factors shown in Figure ~\ref{fig:case_study_676_0.01}. For each rating factor, we show top 3 items with highest predicted scores, each is described by an image and a short description. For each text factor, we visualize top 10 words with highest predicted scores in a cloud. 
%     The bigger a word is, the higher its predicted score by \ourmethod\ is.}
%     % The color of a word cloud is aligned with that of rating factor bounding box via coupling probabilities shown in Figure ~\ref{fig:case_study_11685_0.1}.}
%     % . For example, the purple word cloud is aligned with rating factor 1 with a purple bounding box.}
%     % on the right while some other words appear in item descriptions, which we skip here to save space.\looseness=-1}
%     \label{fig:case_study}
% \end{figure}
% \begin{figure}[!ht]
%     \begin{subfigure}[t]{\columnwidth}
%         \centering
%         \scalebox{0.8}{
%             \includegraphics[width=\columnwidth]{figures/case_study/epsilon/676_0.01.png}
%         }
%         \caption{Alignment matrix produced by \ourmethod.}
%     \end{subfigure}
%     \\
%     \begin{subfigure}[t]{\columnwidth}
%         \centering
%         \scalebox{0.75}{
%             \includegraphics[width=\columnwidth]{figures/case_study/676_case_study.png}
%         }
%         \caption{Illustration of aligning rating and text interest factors. For each rating factor, we show top 3 items with highest predicted scores, each is described by an image and a short description. For each text factor, we visualize top 10 words with highest predicted scores in a cloud. 
%         The bigger a word is, the higher its predicted score by \ourmethod\ is.\looseness=-1}
%     \end{subfigure}
%     \caption{Visualization of aligning interest factors by \ourmethod.}
%     \label{fig:case_study}
%     \vspace*{-0.5cm}
% \end{figure}
\begin{figure}[!ht]
    \centering
    % \scalebox{0.75}{
    \includegraphics[scale=0.35]{figures/case_study/676_case_study.png}
    % }
    \caption{Illustration of aligning interest factors. For rating factors, we show top 3 items with highest predicted scores, described by images and short descriptions. For text factors, we visualize top 10 words with highest predicted scores.} 
    % The bigger a word is, the higher its predicted score by \ourmethod\ is.\looseness=-1}
    \label{fig:case_study}
    % \vspace*{-0.5cm}
\end{figure}