A Review on Designing of Recommendation System

Recommendation systems are essential tools for modern online platforms and services. Taking into account their preferences, interests, and behaviour, they offer consumers personalised recommendations. Recommendation systems can be classified as collaborative filtering, content-based filtering, or hybrid filtering. These technologies are widely used in e-commerce, social networking, music and video streaming, and news articles. Evaluation criteria like precision, recall, and F1-score are used to gauge how effective recommendation systems are. As the amount of data generated by users continues to increase, recommendation systems are likely to become more sophisticated and accurate in the future. This paper provides an overview of recommendation systems, including their types, applications, and evaluation metrics. The paper aims to provide a comprehensive understanding of recommendation systems, their capabilities, and limitations. The rest of the paper is organized as follows. Provides a detailed overview of the different types of recommendation systems. Discusses the applications of recommendation systems in various domains. Describes the evaluation metrics used to measure the effectiveness of recommendation systems. Concludes the paper by summarizing the key findings and discussing future research directions.