Implementation of Renthub System: An Intelligent Online Rental Marketplace with ML-Powered Personalized Product Discovery and Recommendations

The rapid expansion of peer-to-peer rental services has significantly influenced the share economy by connecting consumers with short-term access to diverse rental products. However, existing platforms primarily focus on specific categories, limiting consumer choices and creating a gap in the market. This study introduces RentAll, a comprehensive multi-category rental platform offering access to houses, automobiles, furniture, gadgets, and jewelry, while prioritizing data privacy through anonymized transactions. To enhance user experience, we developed a recommendation system utilizing content-based filtering, cosine similarity, and collaborative filtering through FP-Growth Frequent Itemset Mining to suggest products based on customer behavior. Additionally, a chatbot powered by a Sequence-to-Sequence model using RNN and LSTM units was integrated for real-time customer support. The results demonstrate RentAll's effectiveness in providing a unified rental solution with personalized recommendations. The platform streamlines the rental process, reduces financial strain, and expands product offerings to serve diverse demographics. High user satisfaction is reported due to its user-friendly interface and engaging features, including secure payment processing via Easypaisa. Moreover, the implementation of robust security measures protects user informationand builds trust. In conclusion, RentAll effectively addresses key issues in online rentals by offering a user-friendly platform with diverse rental categories, enhancing consumer convenience and satisfaction while maintaining stringent data protection standards.