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Content-based video recommendation system (CBVRS): a novel approach to predict videos using multilayer feed forward neural network and Monte Carlo sampling method

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Abstract

Video recommendation has become a crucial role in mitigating the semantic gap in recommending the video based on visual features. This article proposed the exploitation of low-level visual features extracted from videos, and the input data to generate relevant recommendations. Initially, the video is pre-processed with Motion Adaptive Gaussian Denoising Filtering, which eliminates noise from video frames and achieves improved efficiency with high quality and video resolution, which requires less computation. After pre-processing, the paper proposed a content-based extraction approach to retrieve the temporal and spatial characteristics. The temporal characteristics represent the dynamic viewpoints of video, including average shot time and object movement, while the spatial characteristics illustrate a static effect, such as colour and lighting key. Subsequently, this utilizes a series of representative visual features to make the video content more accurate. Finally, the work incorporates a deep neural network to predict the video according to the input and the features extracted. The supervised learning algorithm Multilayer feed-forward is therefore proposed, which generated a series of outputs from a given input set (input data and the extracted features). The majority of deep learning solutions deliver deterministic outcomes and do not measure or monitor prediction variance, which can contribute to a loss of faith in automatic evaluation. Subsequently, Monte Carlo’s uncertainty techniques are used to estimate the exact video in accordance with the recommendation. The proposed method is implemented using MATLAB r2020a software with less computation time of 0.999 s and the performance of the proposed method is compared with the different existing methods like MMM, LP-LGSN, and CDPRec. Consequently, the proposed method produces higher performance in terms of precision, recall, F-measures, and nDCG and it produces higher accuracy of 0.94%, respectively.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Gudlavalleru Engineering College, Gudlavalleru, Krishna District, Andhra Pradesh, 521356, India

    Baburao Markapudi

  2. Department of Information Technology, Gudlavalleru Engineering College, Gudlavalleru, Krishna District, Andhra Pradesh, 521356, India

    Kavitha Chaduvula & D.N.V.S.L.S. Indira

  3. Department of Computer Science and Engineering, Krishna University College of Engineering and Technology, Krishna University, Rudravaram, Machilipatnam, Krishna District, Andhra Pradesh, 521004, India

    Meduri V. N. S. S. R. K. Sai Somayajulu

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  1. Baburao Markapudi
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  2. Kavitha Chaduvula
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  3. D.N.V.S.L.S. Indira
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  4. Meduri V. N. S. S. R. K. Sai Somayajulu
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Markapudi, B., Chaduvula, K., Indira, D. et al. Content-based video recommendation system (CBVRS): a novel approach to predict videos using multilayer feed forward neural network and Monte Carlo sampling method. Multimed Tools Appl 82, 6965–6991 (2023). https://doi.org/10.1007/s11042-022-13583-8

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