Enhancing Personalization: A Machine Learning Approach to Modern Recommender Systems

In the digital era where Industrie 4.0 has emerged, most of the application are either web based or mobile based and also, people are becoming very specific in selecting their products and amenities over internet. This preference in choices of users makes it difficult for the service provider to understand which product or service they should provide to the user so that the user can select appropriately and that too in less time. This has led to the use of recommendation system for these purposes. Also, the recommendation system helps manufacturers to understand that which product has higher consumption and which product has lower consumption. This paper tries its best to first explain the foundation concept of recommendation system and its forms. Also, the paper tries its best to deep dive into all the relevant research that has been carried out to understand the merits as well as limitation of if recommendation system. This work deals with various machine learning algorithms such as Neural Collaborative Filtering (NCF), Embedding Layers, Deep Neural Networks (DNN), Matrix Factorization, Hybrid Model: Collaborative Filtering (SVD), Content-Based Filtering. The paper also highlights the vital applications of recommendation system that are useful in real life.