Enhanced Freelance Matching: Integrated Data Analysis and Machine Learning Techniques

The objective of this research is to devise a personalized recommendation system for a freelancing platform to optimize the freelancer project matching process. This enhancement is intended to improve user experience and increase the success rate of projects. The system will recommend projects to freelancers based on their skills and preferences by employing data analysis and machine learning methodologies. The research methodology adheres to the Cross Industry Standard Process for Data Mining (CRISP-DM), incorporating six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The proposed project employs a hybrid recommendation strategy, integrating Content-Based Filtering through KNearest Neighbors (K-NN) and Cosine Similarity, Collaborative Filtering via Singular Value Decomposition (SVD), and recommendations derived from Word2vec. Evaluation metrics such as precision, recall, F1 score, MAP, and MRR are utilized to assess model performance. The results, including precision scores of 0.80 for KNN and 0.728 for SVD, recall scores of 0.60 for KNN and 0.623 for SVD, and F1 scores of 0.69 for KNN and 0.671 for SVD, as well as a MAP of 0.75 and MRR of 0.80 for Word2vec, demonstrate the efficacy of the hybrid recommendation system in delivering accurate and varied project suggestions to freelancers, with a weighted average ensemble learning model emerging as the most effective solution.