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Efficient Machine Learning Algorithms in Hybrid Filtering Based Recommendation System

. Ruchika ; Mayank Sharma ; Syed Akhter Hossain

Nashrīyah-i mudīrīyat-i fannāvarī-i iṭṭilāʻāt, 2023-08, Vol.15 (3), p.134-161 [Periódico revisado por pares]

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Título:
Efficient Machine Learning Algorithms in Hybrid Filtering Based Recommendation System
Autor: . Ruchika ; Mayank Sharma ; Syed Akhter Hossain
Assuntos: collaborative filtering ; content-based filtering ; deep learning ; movie recommendation ; recommender system
É parte de: Nashrīyah-i mudīrīyat-i fannāvarī-i iṭṭilāʻāt, 2023-08, Vol.15 (3), p.134-161
Descrição: The widespread use of E-commerce websites has drastically increased the need for automatic recommendation systems with machine learning. In recent years, many ML-based recommenders and analysers have been built; however, their scope is limited to using a single filtering technique and processing with clustering-based predictions. This paper aims to provide a systematic year-wise survey and evolution of these existing recommenders and analysers in specific deep learning-based hybrid filtering categories using movie datasets. They are compared to others based on their problem analysis, learning factors, data sets, performance, and limitations. Most contributions are found with collaborative filtering using user or item similarity and deep learning for the IMDB datasets. In this direction, this paper introduces a new and efficient Hybrid Filtering based Recommendation System using Deep Learning (HFRS-DL), which includes multiple layers and stages to provide a better solution for generating recommendations.
Editor: University of Tehran
Idioma: Persa

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