## 1 Introduction
There was a positive correlation between binary ratings and sentiment scores as shown in Figure 3b, while no significant correlation was found between ordinal ratings and sentiment scores as shown in Figure 3a. For in-depth analysis, we explored review texts given by users who clicked dislike button while giving ordinal ratings of 4 or 5. In most cases, review texts contained negative opinions as shown in Table 1. For instance, users \(D\) and \(E\) expressed their dissatisfaction by clicking the dislike button and criticized expert's misunderstanding of manuscripts in the review texts while leaving 4-point ordinal ratings. This implies that ordinal ratings may have a bias in expressing a user's negative opinion. Therefore, it is possible to enhance the performances of editor recommendation through refining user opinions by not just incorporating ordinal and binary ratings together but utilizing them selectively, making a feedback rating matrix for performing MF to have more reliable information. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**User**} & **Binary** & **Ordinal** & \multirow{2}{*}{**Review Text**} \\  & **Rating** & & **Rating** & \\ \hline \(A\) & Dislike & 5 & Your editing result is not what I intended.. I was a little disappointed T.T \\ \hline \multirow{3}{*}{\(B\)} & \multirow{3}{*}{Dislike} & \multirow{3}{*}{4} & I always got the results I asked for 3-4 hours ago, \\  & & & but in this time, i got the edited manuscript just on time. \\ \cline{1-1}  & & & It was later than i expected, so I felt uncomfortable. \\ \cline{1-1}  & & & Also, requests to convert all sentences to colloquialism were not reflected. \\ \hline \multirow{2}{*}{\(C\)} & \multirow{2}{*}{Dislike} & \multirow{2}{*}{4} & There is a sentence edited differently from my intention. \\ \cline{1-1}  & & & The editing expert proofread the manuscript to his style, and i was disappointed. \\ \hline \multirow{2}{*}{\(D\)} & \multirow{2}{*}{Dislike} & \multirow{2}{*}{4} & If the editing expert concentrated in considering the sentence structure \\ \cline{1-1}  & & & in the whole paragraph when he edited, it is possible to reflect my intention all. \\ \cline{1-1}  & & & Experts only changed sentences with awkward expressions. \\ \hline \multirow{2}{*}{\(E\)} & \multirow{2}{*}{Dislike} & \multirow{2}{*}{4} & Understand the context and structure of the manuscript first. And then edit. \\ \hline \multirow{2}{*}{\(F\)} & \multirow{2}{*}{Dislike} & \multirow{2}{*}{4} & It seems like the editor used translator. \\ \cline{1-1}  & & & I asked the editing service for conveying the meaning of a sentence accurately. \\ \cline{1-1}  & & & Not a literal translation. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of user feedback logs composed of binary ratings, ordinal ratings, and review texts, where the category of binary ratings are dislike while the those of ordinal ratings are 4 or 5. Figure 3: Boxplots representing distributions of review texts’ sentiment scores according to (**a**) ordinal ratings and (**b**) binary ratings. To this end, we propose an MF based expert recommender system for manuscript editing services. MF is adopted to explore the inherent characteristics of a manuscript such as writing style and paragraph composition, which are difficult for humans to detect. Moreover, ordinal and binary ratings are selectively utilized to refine user opinions and alleviate data sparsity problem. The two types of user feedback are combined in various ways to maximize the recommendation performances. Specifically, the proposed method is composed of three steps. First, a feedback rating matrix is constructed by combining ordinal and binary ratings. Second, user opinions are inferred by performing MF on the feedback rating matrix. Lastly, the optimal editing expert is selected for a user based on the result of the second step. The rest of paper is organized as follows. The proposed method is introduced and explained in Section 2. Section 3 presents experiment settings and results.