This study explores advanced techniques in book recommendation systems, which are integral components of contemporary online retail and e-commerce platforms. Traditional recommendation models have largely utilized algorithms such as K-nearest neighbors and cosine similarity. While effective to a certain extent, these methods often fail to generate sufficiently personalized and context-aware suggestions. To address these limitations, we introduce a hybrid recommendation framework that combines singular value decomposition (SVD) with cosine similarity, incorporating both content-based filtering and collaborative filtering strategies. Cosine similarity serves to identify items with similar user rating patterns; however, it does not account for latent variables that may influence user preferences. By integrating SVD-based matrix factorization, the proposed approach captures these hidden factors, offering a more nuanced understanding of user–item interactions. The system’s effectiveness is assessed using standard evaluation metrics, including precision, recall, normalized mean absolute error, root-mean-squared error, and mean absolute error. Experimental results indicate that approximately 80% of the top-k recommendations are relevant, with a precision score of 0.80. Overall, the findings suggest that hybrid models combining SVD with cosine similarity significantly enhance recommendation accuracy compared to approaches that rely solely on similarity measures. Beyond book recommendations, this framework can be extended to domains such as movies, music, and product recommendations, thereby contributing to the advancement of personalized and user-centric recommendation systems.