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Recommendation systems aim to decipher user interests, preferences, and behavioral patterns automatically. However, it becomes trickier to make the most trustworthy and reliable recommendation to users, especially when their hardest earned money is at risk. The credibility of the recommendation is of magnificent importance in crowdfunding project recommendations. This research work devises a hybrid machine learning-based approach for credible crowdfunding projects’ recommendations by wisely incorporating backers’ sentiments and other influential features. The proposed model has four modules: a feature extraction module, a hybrid LDA-LSTM (latent Dirichlet allocation and long short-term memory) based latent topics evaluation module, credibility formulation, and recommendation module. The credibility analysis proffers a process of correlating project creator’s proficiency, reviewers’ sentiments, and their influence to estimate a project’s authenticity level that makes our model robust to unauthentic and untrustworthy projects and profiles. The recommendation module selects projects based on the user’s interests with the highest credible scores and recommends them. The proposed recommendation method harnesses numeric data and sentiment expressions linked with comments, backers’ preferences, profile data, and the creator’s credibility for quantitative examination of several alternative projects. 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. We have also evaluated our credibility assessment technique on different categories of the projects, i.e., suspended, canceled, delivered, and never delivered projects, and achieved satisfactory outcomes, i.e., 93%, 84%, 58%, and 93%, projects respectively accurately classify into our desired range of credibility.

Effectiveness of Machine Learning Approaches Towards Credibility Assessment of Crowdfunding Projects for Reliable Recommendations