Authors: Labdhi Jain, Rajesh Dhakad Associate Professor
Abstract: In the current digital era, the volume of data produced every second is staggering, making it challenging for users to find relevant information. Recommendation systems utilize extensive data and data mining techniques to analyze large amounts of data and provide accurate, personalized sug- gestions. Recommendation systems are information filter- ing systems that provide particular suggestions for items that are most pertinent to a particular user or a group of users. The algorithms and methods used for recommender systems are Content-Based Filtering, Collaborative Filtering, and Hybrid Methods. Recommendation systems include diverse applications and domains such as books, e-commerce ser- vices, social network services, movies, and tourism services. Key evaluation metrics of different recommender systems are discussed to provide insights into the assessment of mod- els and the optimization of their performance. Globally, recommendation systems have become important. The pur- pose of this paper is to include and give knowledge of each method, from a traditional-based recommendation system to a deep learning-based recommendation system. By synthe- sizing current trends, challenges, and future research direc- tions, this paper offers a comprehensive understanding of the recommendation system for both researchers and industry professionals.
DOI: http://doi.org/
