## 6 Conclusions  
We have proposed the methodology for measuring a project's credibility to build a recommendation system. The proposed method uses textual and non-textual data. This system is developed to help the users in selecting reliable and trustworthy options in their preferred categories. The proposed method is a hybrid model of LDA-LSTM and topic modeling that joins the benefits of both (1) LSTMs that captures time dependencies for class and topic prediction and (2) topic modeling that extracts topics that nicely summarize the content. A case study on crowdfunding is performed to analyze and test the proposed system's behavior. We have also embedded an optimized recommendation strategy based on a project's credibility.  
This study aims to overcome the limitations of topic models and deep learning and get the most out of both approaches. The main objectives include:  
* Finding ways to preserve the contextual dependencies as traditional topic models are based on the bag-of-words approach, so there is a high probability of missing contextual and temporal dependencies.
* Recommendation tools are in use for a long time now; finding the recommended project's credibility is a potential target of this research.  
This joint model of LDA-LSTM exploits words and topic embedding, and the temporal data attain 96% accuracy in predicting the topic categories accurately. The topics classes discovered were also evaluated in the context of helping investors identify suspicious campaigns. The prediction quality can be improved if we find out different configurations of comments concerning a project's timeline. We experimented with this by dividing the comments into five various batches of comments. We have not considered projects that have less than 50 comments to maintain the quality of the results.  
Many developed applications for recommendation systems in different fields have been proposed. Our proposed approach is a novel approach to recommend a credible crowdfunding project to the best of our knowledge. Moreover, none of the works have focused on crowdfunding comments to find discussion trends and their impact on project credibility. Hence, in crowdfunding, this approach can be used to recommend safe or secure projects to investors. In Table 13, we show a comparison analysis of our proposed model with existing models of recommender systems. In [47; 48; 49; 50], the authors use Kickstarter for predictions of the project's success. Others are related to taking comments and updates for estimating the completion of projects and then recommend them to the user. We summarize this research work's contributions: (1) a hybrid method is proposed for reliable and promising recommendations. This approach can model user preferences and word representations in a typical and dynamic style to empower the active measurement of the semantic similarity among the user's preferences and the words. (2) The proposed algorithm is to infer the dynamic embeddings of both the documents and words. We offer a credibility measurement approach for reliable recommendations. The results show that our proposed method outperforms similar state-of-the-art methods significantly.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Recommendation** & \multirow{2}{*}{**LDA**} & \multirow{2}{*}{**LDA-LSTM**} & **Language** & \multirow{2}{*}{**Optimization**} & \multirow{2}{*}{**Comments**} & **Credibility** \\
**Systems** & & & & & & Assessment \\ \hline
[49] & X & X & ✓ & X & X & X \\
[50] & ✓ & X & ✓ & X & X & X \\
[51] & ✓ & X & ✓ & X & X & X \\
[52] & ✓ & X & ✓ & X & X & X \\
[53] & X & X & ✓ & X & X & X \\
[54] & ✓ & X & ✓ & X & ✓ & X \\ Proposed Model & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of our proposed approach with existing recommender systems.