Hesitant fuzzy clustering with convolutional spiking neural network for movie recommendations Article Swipe

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YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.11591/ijeecs.v36.i3.pp1849-1856

The movie recommender system is one of the most influential and practical tools for aiding individuals in quickly selecting films to watch. Despite numerous academic efforts to employ recommender systems for various purposes, such as movie-watching and book-buying, many studies have overlooked user-specific movie recommendations. This paper introduces a novel approach for movie recommendations that combines the hesitant fuzzy clustering with a convolutional spiking neural network movie recommender system. The initial step involves acquiring input data from benchmark datasets like MovieLens 100K and MovieLens 1M. Further, content-based features are extracted from the dataset using ternary pattern and discrete wavelet transforms. After that hesitant fuzzy linguistic Bi-objective clustering (HFLBC) is applied for cluster selection based on the extracted features. Subsequently, a movie recommender scheme utilizing a convolutional spiking neural network is introduced to predict user film preferences. The efficiency of the proposed model is compared to existing methods such as multi-modal trust-dependent recommender scheme and graph-dependent hybrid recommendation scheme. The results show a significant improvement, with the proposed model achieving 13.79% and 16.47% higher accuracy than the existing methods. The findings highlight the potential of proposed system in enhancing the accuracy and personalization of movie recommendations.

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Convolutional Neural Network

Cluster Analysis

Computer Science

Artificial Intelligence

Data Mining

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Convolutional neural network Cluster analysis Computer science Artificial intelligence Fuzzy logic Fuzzy clustering Data mining Pattern recognition (psychology)

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Type: article
Language: en
Landing Page: https://doi.org/10.11591/ijeecs.v36.i3.pp1849-1856
OA Status: diamond
Related Works: 10
OpenAlex ID: https://openalex.org/W4403020935

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DOI: https://doi.org/10.11591/ijeecs.v36.i3.pp1849-1856

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Title: Hesitant fuzzy clustering with convolutional spiking neural network for movie recommendations

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Type: article

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Language: en

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Publication year: 2024

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Publication date: 2024-10-01

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Authors: Vineet Shrivastava, Suresh Kumar

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Landing page: https://doi.org/10.11591/ijeecs.v36.i3.pp1849-1856

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Concepts: Convolutional neural network, Cluster analysis, Computer science, Artificial intelligence, Fuzzy logic, Fuzzy clustering, Data mining, Pattern recognition (psychology)

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