## 3 Proposed Credibility Formulation for Project Recommendation Based on Hybrid Model
### Project Credibility Estimation  
In this section, we present a detailed explanation of the credibility module. The overall process of deriving formulas steps by steps to estimate the credibility of a project is delivered. Trust is an ultimate significant element in any domain that helps to gain the customer's confidence. It is valid for e-commerce sites and online social networks, as well. Therefore, multiple trust-aware recommender systems are being proposed that adopt user's trust statements and their personal or profile data to improve the quality of recommendations considerably. As we target crowdfunding projects, we aim to formulate an equation to calculate any project's credibility before recommending it to a user. A highly credible recommendation is a project that most likely reflects the user-defined interests and categories with higher chances of its delivery. It must also reflect the lowest probability of factors that can disturb the project's trustworthiness, such as communication delays and less frequent updates, etc. A credible project can precisely be defined as a  
Figure 2: Latent Dirichlet allocation (LDA) process. project with the maximum likelihood of completing and delivering to the backers within the promised period. Various factors are associated with a project's credibility; we define and link a documents' credibility with its estimated authenticity score range. A project's authenticity is a multi-fold view of different and latent aspects, such as latent aspects of a creator's profile and all his or her external social links. It also involves the frequency of account usage and updates from creators. In other words, keeping the backers up to date with each development or progress in the project can earn more credibility points. In addition to that, factors such as the most frequent keywords used, promises related to product delivery or rewards delivery, and investors' sentiments are also crucial. These sentiments of backers are discovered during the LDA process to find latent topics in their comments. There can be multiple topics in a document, and each topic represents a particular class of sentiments. As shown in Table 2[45], we identified 12 topic classes labeled Topic-1 to Topic-12. The number of topics was varied between 2 and 30 during the experiments to find the optimal number of topics. The coherence score was increasing as the number of topics was growing. We selected and evaluated the topics based on the coherence score before flattening out, i.e., 12 topics. After training LSTM, the classification of each comment is done into one of these topic classes. We have divided these sentiment classes into three categories, and this division is customized based on the problem, i.e., credibility assessment. These categories are referred to as A, B, and C. Category A is responsible for extremely negative comments, which is represented by Topic-4 to Topic-7; category B means negative reviews, which is characterized by Topic-1 to Topic-3; and category C is representing positive or neutral reviews which are represented by Topic-8 to Topic-12. More emphasis is laid on the negative  
Figure 3: Detailed view of the proposed approach. comments because the negative comments and reviews significantly impact the viewer's mind and decision-making process than positive comments regarding credibility or trust. Therefore, we divided the negative comments into extremely unfavorable class A and negative class B. To evaluate our selected topics, we measured the agreement between two raters using Cohen's Kappa coefficient [46] and followed the process mentioned in [47] to assess our LDA model. Two students (student A and student B) from different laboratories who were unaware of our proposed methodology and had no prior knowledge about the list of LDA topics were requested to extract topics from 250 sampled reviews. Student A and student B were not allowed to communicate or discuss their thought process behind labeling each review. Student A and student B could identify 9 and 11 topics, respectively.