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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Dayal Kumar Behera
KIIT Deemed to be University

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Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine Dayal Kumar Behera; Madhabananda Das; Subhra Swetanisha; Prabira Kumar Sethy
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp445-452

Abstract

One of the most commonly used techniques in the recommendation framework is collaborative filtering (CF). It performs better with sufficient records of user rating but is not good in sparse data. Content-based filtering works well in the sparse dataset as it finds the similarity between movies by using attributes of the movies. RBM is an energy-based model serving as a backbone of deep learning and performs well in rating prediction. However, the rating prediction is not preferable by a single model. The hybrid model achieves better results by integrating the results of more than one model. This paper analyses the weighted hybrid CF system by integrating content K-nearest neighbors (KNN) with restricted Boltzmann machine (RBM). Movies are recommended to the active user in the proposed system by integrating the effects of both content-based and collaborative filtering. Model efficacy was tested with MovieLens benchmark datasets.
Co-Authors Madhabananda Das Prabira Kumar Sethy Subhra Swetanisha