<p>The prevalence of spam reviews on e-commerce platforms undermines consumer trust, distorts product evaluations, and often leads to suboptimal purchasing decisions. This study proposes a spam-aware recommendation framework that first detects spam reviews using machine learning and deep learning models based on semantic, sentiment, and metadata features, and then integrates these filtered credible reviews into personalized product recommendations. Rather than treating spam detection and recommender design as isolated tasks, this work bridges the gap between review credibility and user preference modelling to improve recommendation accuracy. By ensuring that only trustworthy reviews contribute to recommendation generation, the proposed approach enables users to receive more reliable and preference-aligned suggestions, thereby supporting more informed and confident purchasing decisions.</p>