Recommender Systems: An Overview, Challenges and Optimization Strategies, Future Scope

In an era characterized by information overload and the proliferation of digital content, recommender systems (RS) have emerged as indispensable tools for assisting users in navigating the vast landscape of choices. These systems employ sophisticated algorithms and methodologies to anticipate user preferences and provide tailored suggestions, thereby enhancing user experience and engagement across a diverse range of online platforms. They have made giant leaps of progress in accuracy and performance. Despite the revolutionary potential, recommender systems are not immune to challenges that can affect their efficiency. The key challenges include data sparsity-The uneven distribution of data regarding user-item interactions, cold start, scalability, ever changing priorities of user making the previous results obsolete .In This paper we set a stage for meticulous review of the RS and their applications in various fields ,the taxonomies involved in RS, discussing the two primary categories : collaborative filtering and content-based filtering. This paper at the later stage exposes us to the challenges in RS, the metrics used to evaluate the impact of the strategies used to overcome these challenges. Furthermore, this paper also envisions the future of RS which may open new research directions in this domain. Index: introduction, Types of RS, Challenges in RS, Optimization Strategies, Evaluation Metrics, Case Studies, Future Directions, Conclusion, References. Appendices.