## 3 Proposed Credibility Formulation for Project Recommendation Based on Hybrid Model
### Project Credibility Estimation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Authenticity Parameters** & **Description** & **Notations** \\ \hline \multicolumn{5}{c}{**Content related Features**} \\ \hline
1 & Sentiments (-ve) & Percentage of comments that belong to class A & \(\mathit{eNegA}\) \\ \hline
2 & Sentiments (-ve) & Percentage of comments that belong to class B & \(\mathit{NegB}\) \\ \hline
3 & Sentiments (+ve) & Percentage of comments that belong to class C & \(\mathit{PosC}\) \\ \hline
4 & Readability score & This score reflects the clarity of content, i.e., \(\mathit{\text{how easy or difficult it is to understand.}}\) & \(\text{read}_{\text{Score}}\) \\ \hline \multicolumn{5}{c}{**Profile related Features**} \\ \hline
5 & Profile picture & To check profile picture exists or not & \(\mathit{picY}\) \\ \hline
6 & Social Links & The total number of links (external or social network links, e.g., Facebook, Twitter, etc.), & Links\({}_{\text{Ext}}\) \\ \hline
7 & Communication delay & The time delay between two consecutive posts (update or comment) by the creator & delay\({}_{\text{comm}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Definitions of the parameters of authenticity. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Topic Classes**} & \multicolumn{2}{c}{**Student A-LDA**} & \multicolumn{2}{c}{**Student B-LDA**} & \multicolumn{2}{c}{**Student A–Student B**} \\ \cline{2-7}  & **Kappa** & **Overlap** & **Kappa** & **Overlap** & **Kappa** & **Overlap** \\ \hline Topic-1 & 0.67 & 207 & 0.65 & 203 & 0.69 & 223 \\  & (Moderate) & (11) & (Moderate) & (10) & (Moderate) & (22) \\ Topic-2 & 0.69 & 219 & 0.63 & 211 & 0.79 & 234 \\  & (Moderate) & (17) & (Moderate) & (18) & (Moderate) & (24) \\ Topic-5 & 0.81 & 223 & 0.78 & 219 & 0.85 & 239 \\  & (High) & (19) & (Moderate) & (17) & (High) & (25) \\ Topic-6 & 0.83 & 227 & 0.81 & 226 & 0.89 & 238 \\  & (High) & (21) & (High) & (19) & (High) & (21) \\ Topic-7 & 0.82 & 226 & 0.80 & 227 & 0.87 & 230 \\  & (High) & (19) & (High) & (15) & (High) & (20) \\ Topic-10 & 0.76 & 220 & 0.83 & 227 & 0.85 & 236 \\  & (Moderate) & (20) & (High) & (21) & (High) & (19) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reliability Assessment of LDA model using Cohen’s Kappa coefficient. Hence from above Equations (2) and (3), we have  
\[\mathit{Eligibility}_{\mathit{project}}= \begin{cases}0&\mathit{ favorable}\\ <0\geq-0.4&\mathit{can be considered}\\ <-0.4&\mathit{un favorable}\end{cases}  \tag{4}\]  
Therefore, based on Equation (1) and following the conditions in Equation (4), we can list all possible scenarios of eligibility in Table 5. The content in \(\mathit{eNegA}\) is extremely unfavorable as one can sense fears, suspicion, and frustrations in it. Therefore, this category is handled independently to alleviate the probability of any unreliable recommendation. For a reliable project, it must be free from any of the comments in \(\mathit{eNegA}\) category. Thus, we used this to set our eligibility criteria. The objective function targets getting the maximum percentage of positive comments, i.e., category C. It also targets to get the maximum number of social links of the project's creator. In crowdfunding, a backer's faith and confidence rely on the content authenticity and creator's limpidity. Therefore, these aspects are fundamental to a project's success. Table 4 shows that the factor \(\mathrm{delay}_{\mathrm{comm}}\) is one prime feature of the project, representing a creator's communication styles such as his updates and comments. This feature, \(\mathrm{delay}_{\mathrm{comm}}\) can be defined as the average time gap between any consecutive posts by the project creator in an update or a comment. It shows the communication rate of a project creator towards the development of a project. Due to the impact of \(\mathrm{delay}_{\mathrm{comm}}\), the project's authenticity will be damaged if the communication delay upsurges. After observing and estimating all the relevant features, all the values are normalized between 0 to 1.