Survey papers in recommender systems provide a comprehensive overview of algorithms, models, and applications that help users discover relevant information, products, or services in large-scale datasets. These surveys review traditional techniques such as collaborative filtering, content-based filtering, and hybrid approaches, along with advanced methods like matrix factorization, deep learning models (CNNs, RNNs, autoencoders, transformers), reinforcement learning, and graph-based recommenders. They also discuss evaluation metrics (precision, recall, NDCG, diversity, novelty), scalability challenges, cold-start problems, and privacy concerns. Recent survey papers highlight the role of context-aware, explainable, and federated recommender systems, as well as applications in e-commerce, healthcare, IoT, social media, and entertainment platforms. By consolidating progress and identifying open challenges, survey papers in recommender systems guide researchers and practitioners in designing adaptive, efficient, and trustworthy recommendation frameworks.