Affiliation:
1. School of Computer Science and Engineering, Vellore Institute of Technology , India
Abstract
Abstract
Lack of personalization, rating sparsity, and cold start are commonly seen in e-Learning based recommender systems. The proposed work here suggests a personalized fused recommendation framework for e-Learning. The framework consists of a two-fold approach to generate recommendations. Firstly, it attempts to find the neighbourhood of similar learners based on certain learner characteristics by applying a user-based collaborative filtering approach. Secondly, it generates a matrix of ratings given by the learners. The outcome of the first stage is merged with the second stage to generate recommendations for the learner. Learner characteristics, namely knowledge level, learning style, and learner preference, have been considered to bring in the personalization factor on the recommendations. As the stochastic gradient approach predicts the learner-course rating matrix, it helps overcome the rating sparsity and cold-start issues. The fused model is compared with traditional stand-alone methods and shows performance improvement.
Reference53 articles.
1. 1. Karataev, E., V. Zadorozhny. Adaptive Social Learning Based on Crowdsourcing. – IEEE Transactions on Learning Technologies, Vol. 10, April 2017, No 2, pp. 128-139.10.1109/TLT.2016.2515097
2. 2. KPMG & Google (2017). Online Education in India: 2021. Accessed 21 Janurary 2019. https://assets.kpmg.com/content/dam/kpmg/in/pdf/2017/05/Online-Education-in-India-2021.pdf/
3. 3. Ricci, F., L. Rokach, B. Shapira. Recommender Systems: Introduction and Challenges. – Recommender Systems Handbook, Boston, MA, USA: Springer, 2015, pp. 1-34.10.1007/978-1-4899-7637-6
4. 4. Wan, S., Z. Niu. A Hybrid e-Learning Recommendation Approach Based on Learners’ Influence Propagation. – IEEE Transactions on Knowledge and Data Engineering, January 2019.10.1109/TKDE.2019.2895033
5. 5. Adomavicius, G., A. Tuzhilin. Context-Aware Recommender Systems. – Recommender Systems Handbook, Boston, MA, USA: Springer, 2011, pp. 217-253.10.1007/978-0-387-85820-3_7
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献