A Nobel Approach to Recommend Online Courses by using Hybrid Recommendation System
Sumita Gupta, Rana Majumdar, Sapna Gambhir · 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · 2022
The Recommender systems, also known as recommendation engines are a relatively new but extremely indemand machine learning application being extensively used by websites. They achieve the function of a highly intelligent and responsive virtual salesman on the website by recommending or pitching an idea to the customers in which he/she might be potentially interested. The primary purpose of recommender systems is broadening the profit of the enterprise by simulating sale of maximum products to a customer and to enhance the user experience of the website. Such recommender systems are being widely developed using machine learning concepts, implemented by the Python language, by online enterprises big and small all across the globe. This work exhibits a recommender scheme for an e-learning website which bids various online courses to its users for learning new skills on an online platform. A composite method is functional to implement the same which in turn uses the two basic methods namely content based and collaborative filtering in a non-inclusive manner.