Paper Title
A Hybrid Recommender System for E-Learning Based on Content Mining

During the last few decades, with the rise of YouTube, Amazon, Netflix and many other such web services, recommender systems have taken more and more place in our lives. From e-commerce to online advertisement recommender systems are today unavoidable in our daily online journeys. In this presented work the web content mining and web usages mining is the main area of investigation. Using these concepts we work for designing web content recommendation system for an e-learning platform. We proposed a hybrid recommendation system model that works on student learning web data and produces the recommendation to students for their course material. In this context web pages and URLs are used with content mining concept for preparing the information retrieval system. Additionally the HMM and K-Means clustering developed to recommend the study material according to student learning behavior. The proposed model is evaluated on experimental dataset and compared with two similar variants based on HMM and apriori algorithm. The results show the proposed model works fine for recommending study material. Keywords - Web Data Mining, Recommender System, Feature Selection, Web logs.