Volume 18, No. 6, 2021

Optimal HMM Based Recommendation System For Teaching Faculty


Kapil Chourey and Atul D. Newase

Abstract

The digital marketing paradigm has seen revolutionary change with the augmentation of recommender systems. The implementation of recommender systems in the fields of e-commerce, entertainment, digital publicity, healthcare, etc has boosted the business many folds. It has also improved the comfort and user experience through appropriate suggestions. However, these recommender systems have not been exploited much in the education field. This paper presents an intelligent recommender system based on machine learning to present a suggestion framework for the teaching faculty. It utilizes various performance indices to derive the recommendations which can greatly improve the overall education sector in terms of studentís academic and research performance. The accurate recommendation in this paper is achieved by modified HMM framework where the tuning parameters have been optimized by particle swarm optimization (PSO). The performance of the proposed systems has been verified through the experimental study and the accuracy has been found to be more than 90%.


Pages: 6876-6883

Keywords: Recommender Systems, Collaborative Filtering, Hidden Markov model, Changing Preference, Dynamic Models, Latent class models, particle swarm optimization.

Full Text