A Survey on Recommendation System Techniques

The main objective of recommendation systems (RS) is to analyze user behavior and recommend important items or services that users might be interested in. Recommendation systems have grown in popularity in various domains such as information technology and e-commerce. They achieve this by customizing recommendations based on individual preferences, efficiently filtering options from a vast pool, and enabling users to discover content that matches their interests. To generate personalized suggestions, numerous recommendation techniques have been developed, including collaborative filtering, content-based filtering, knowledge-based recommendation systems, and others. In addition, hybrid recommendation systems have been proposed to address the limitations of individual methods by combining various techniques.. Our article provides an overview of diverse recommendation techniques, their fundamental approaches, challenges, solution and has equally looked at different solutions to these challenges faced by modern recommender systems. It also recommends promising avenues for future directions.