A Survey on Recommendation System Techniques

Abstract

The primary objective of recommender systems (RS) is to analyze user behavior and propose relevant items or services that users would find appealing. Recommender systems have gained significant prominence 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. Numerous recommendation techniques have been developed to generate personalized suggestions, including collaborative filtering, content-based filtering, knowledge-based recommendation systems, and other approaches. Furthermore, hybrid recommendation systems have been proposed to address the limitations of individual methods by combining different techniques. This paper presents an overview of diverse recommendation methods, their fundamental approaches, challenges, solution and have equally looked at different solutions to these challenges faced by modern recommender systems. It also recommends promising avenues for future directions.