## 1 Introduction  
Recommendation systems aim to assist users in daily decision-making processes and are being utilized by perpetually developing online business ventures. Crowdfunding is a platform that plays the role of a venture capitalist for entrepreneurs with creative minds. The recommendation in crowdfunding becomes trickier and complicated than offline businesses due to many challenges, such as information scrutiny and less proficient investors [1]. Moreover, online data is less reliable and inclined to alteration, making it difficult for investors to rely on a new business idea [2]. Therefore, the credibility assessment of crowdfunding projects becomes an absolute necessity to mitigate the risk of fraud. Crowdfunding is undoubtedly becoming popular as a study [3] shows that approximately 6,445,080 fundraising campaigns were hosted in 2019, with gaming companies being the most successful in generating profit. It is also predicted that the growth of transaction rate annually will reach up to 5.8%, resulting in a total amount of 1180.5 million dollars by 2024 [4]. Despite the remarkable development and inexhaustible possibilities that crowdfunding provides, the challenges and risks of trust, reliability, transparency, etc., are equally daunting, seemingly mounting ones. This study attempts to plug that credibility gap by analyzing and filtering key players' features towards trust-building among investors and the creator. In addition to basic campaign features, we also concentrate on the comments section of the crowdfunding sites, which plays a significant role in fighting against the considerable risks of deceitful online events. In this paper, we propose a credibility formulation for project recommendations based on a hybrid model. Our proposed architecture has four modules: a text analysis module for project comments, a deep learning module, a credibility estimation module, and a recommendation module. The input data is based on comments-related features and other project-related features. The comment-related features are derived from a comment section of a campaign where users leave their feedback about particular topics; other project-related features include project funding goal, creator's experience, number of images/videos/updates/comments, etc. We perform tokenization, streaming, stop words removal, and data normalization in the data preprocessing layer. In the next step, we perform parameters estimation and topic modeling through latent Dirichlet allocation (LDA). LDA clusters the words with the same meaning in a single topic and is passed to the long short-term memory (LSTM) layer, where the input data consists of the word embeddings, topic embeddings per time-step, and topic distributions. The LSTM is then trained against new comments to generate sentiments, i.e., positive, negative, and neutral sentiments. As the output from the LSTM layer, we get topic class, accuracy, and project classification. These results are used in the recommendation module to equate and analyze the product's credibility through sentiment score and authenticity calculation. For optimization, we compute the objective function that has both maximization and minimization function. We also formulate the authenticity score and credibility of the project. Through our developed Equation, we get the credibility score and recommended product as our output. We have developed various equations step by step to build the recommendation system considering the critical aspects of positive and negative comments from users and mentioned how authenticity and credibility inter-related for project evaluation are. Our results show that the proposed approach is feasible for all scenarios and achieves high accuracy in recommendation result and authenticity level evaluation and low error rate. The proposed model's evaluation depicts that credibility assessment based on the hybrid machine learning approach contributes efficient results (with 98% accuracy) than existing recommendation models.