The challenges that arise from the initial phase of recommender systems, known as cold start problems, pose a variety of obstacles during the setup and utilization of these systems. This is particularly evident when incorporating new products and welcoming new users to the platform. This hinders organizations from fully grasping the business worth of recommender systems, as they need to allocate resources for marketing and operations to attract fresh users and endorse novel products. Appreciation to recommender system researchers, who have conducted numerous studies to address cold start challenges. Nevertheless, there has been a scarcity of recent research focused on integrating these methodologies and algorithms. This study's extent is confined to a methodical examination of existing literature, excluding any experimental analysis for comparing and suggesting optimal strategies along with their applicable contexts in cold start situations.