Open Access

ARTICLE

Content-Based Movie Recommendation System Using MBO with DBN

by S. Sridhar^1,*, D. Dhanasekaran², G. Charlyn Pushpa Latha³

1 Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
2 Saveetha Institute of Medical and Technical Sciences, Chennai, India
3 Department of IT, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India

* Corresponding Author: S. Sridhar. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 3241-3257. https://doi.org/10.32604/iasc.2023.030361

Received 24 March 2022; Accepted 26 April 2022; Issue published 17 August 2022

Abstract

The content-based filtering technique has been used effectively in a variety of Recommender Systems (RS). The user explicitly or implicitly provides data in the Content-Based Recommender System. The system collects this data and creates a profile for all the users, and the recommendation is generated by the user profile. The recommendation generated via content-based filtering is provided by observing just a single user’s profile. The primary objective of this RS is to recommend a list of movies based on the user’s preferences. A content-based movie recommendation model is proposed in this research, which recommends movies based on the user’s profile from the Facebook platform. The recommendation system is built with a hybrid model that combines the Monarch Butterfly Optimization (MBO) with the Deep Belief Network (DBN). For feature selection, the MBO is utilized, while DBN is used for classification. The datasets used in the experiment are collected from Facebook and MovieLens. The dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching recommendations. Each file is compared with features that prove the features will support movie recommendations. The proposed model’s mean absolute error (MAE) and root-mean-square error (RMSE) values are 0.716 and 0.915, and its precision and recall are 97.35 and 96.60 percent, respectively. Extensive tests have demonstrated the advantages of the proposed method in terms of MAE, RMSE, Precision, and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm (FCM-BAT), Collaborative filtering with k-NN and the normalized discounted cumulative gain method (CF-kNN+NDCG), User profile correlation‑based similarity (UPCSim), and Deep Autoencoder.

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Keywords

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