A Hybridized Deep Learning Strategy for Course Recommendation

Abstract

Recommender systems have been actively used in many areas like e-commerce, movie and video suggestions, and have proven to be highly useful for its users. But the use of recommender systems in online learning platforms is often underrated and less likely used. But many of the times it lacks personalisation especially in collaborative approach while content-based doesn't work well for new users. Therefore, the authors propose a hybrid course recommender system for this problem which takes content as well as collaborative approaches and tackles their individual limitations. The authors see recommendation as a sequential problem and thus have used RNNs for solving the problem. Each recommendation can be seen as the new course in the sequence. The results suggest it outperforms other recommender systems when using LSTMs instead of RNNs. The authors develop and test the recommendation system using the Kaggle dataset of users for grouping similar users as historical data and search history of different users' data.