Recommender System for Online Learning Using Personalization with K-Means Clustering Algorithm

Abstract

Recommender Systems are software tools and techniques providing suggestions for items to be of use to a user. Recommder systems have proven to be valuable means for online users to cope with the information overload and have become one of the most powerful and popular tool in electronic commerce. Clustering is one of the fudamental operations that are similar to one another within the same cluster and disimilar to data points in other clusters. This system is the implementation of Computer Courses recommender system using K-means Clustering. User profile is used to calculate similar user’s biography and to access the amount of knowledge that user has attained at any point within the process. A user-based collaborative filtering algorithm collects user profiles, which are asumed to present the preferences of many different individuals, and makes recommendations by finding peers with like profiles. It can generate recommended lessons for old users as well as new users.