Recommendation systems play a crucial role in modern information retrieval, e-commerce, and personalized content delivery. This paper provides a comprehensive review of recommendation systems, covering key concepts, methodologies, and applications. It examines different types of recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid approaches, along with evaluation metrics and challenges. Our automatic recommendations and pricing system application aimed at assisting users in selecting and purchasing the optimal PC or laptop aligns with the modern demand for streamlined technology decisions. This innovative app serves as a com-prehensive tool, harnessing user input to curate personalized recommendations while offering access to an extensive database of computer products. Our main contribution is improving the traditional collaborative filtering approach with a novel weighting scheme. We introduce a dynamic weighting mechanism that considers the recency and relevance of interactions to improve the accuracy and personalization of recommendations. Our recommendation systems platform, implementing a novel weighting scheme, observed a 20% increase in click-through rates (CTR) due to more relevant product recommendations. The paper also discusses emerging upcoming patterns and directions in recommendation system research.