This study introduces an adaptive arbitrage trading framework that integrates an intelligent recommender system with Quantum Random Forest Regression and quantum signal generation to enhance decision-making in multi-currency and financial markets. While recommender systems are commonly applied in domains such as e-commerce and multimedia personalization, their adoption in algorithmic trading remains limited. Building on the quantum arbitrage architecture developed in this work featuring Qiskit-based qubit measurements, probabilistic signal generation, SQLite trade logging, and a real-time Streamlit interface the proposed system unifies quantum-inspired computation with machine learning to recommend optimal arbitrage opportunities, strategy configurations, and asset allocations tailored to each trader's style. The Quantum Random Forest Regression model forms the analytical core, using quantum- enhanced feature sampling and nonlinear pattern extraction to interpret high-dimensional market data, volatility changes, economic indicators, sentiment streams, and historical trade performance captured through the application. Unlike traditional trading algorithms that generate static buy/sell signals, this approach continuously delivers dynamic predictive values, probabilistic confidence levels, and personalized strategy recommendations that adapt to evolving market conditions. By combining quantum measurement randomness, model-driven forecasting, and a recommender architecture, the framework demonstrates how quantum computing principles and AI-driven inference can significantly improve predictive accuracy, risk control, and execution efficiency in arbitrage trading representing a major step toward next-generation intelligent financial systems.