Recommendation systems play a pivotal role in assisting users in fnding items, services, or products that align with their preferences. These systems typically rely on personal parameters, user reviews, or a combination of both to make apt recommendations. This survey article delves into the topic of similarity measures in collaborative recommender systems, with a particular emphasis on exploring various existing measures and their implications. The article examines different similarity measures namely ”Pearson Correlation Coefficient, Vector similarity, Adjusted Cosine, Jaccard, few distance-based metrics, PIP similarity, and Bhattacharyya measure”, the heart of collaborative filtering. The PCC similarity metric outperformed other approaches as per the comparative analysis results. This paper covers the mathematical basis, practical applications, limitations, and implications of the aforementioned approaches for recommendation systems. The paper emphasizes the critical role of selecting the right measure, as it directly influences the recommendation process’s accuracy and effectiveness.