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Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5–7, 2024, Proceedings, Part-I

Research Article

A Comprehensive Analysis of Machine Learning and Deep Learning Based Product Recommendation System

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-77075-3_25,
        author={GKalyan Chakravarthi and Raghvendra Kumar and Ssvr Kumar Addagarla},
        title={A Comprehensive Analysis of Machine Learning and Deep Learning Based Product Recommendation System},
        proceedings={Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5--7, 2024, Proceedings, Part-I},
        proceedings_a={IC4S},
        year={2025},
        month={2},
        keywords={Content based Filtering Decision-making process Hybrid Filtering Collaborative Filtering and Recommendation Systems},
        doi={10.1007/978-3-031-77075-3_25}
    }
    
  • GKalyan Chakravarthi
    Raghvendra Kumar
    Ssvr Kumar Addagarla
    Year: 2025
    A Comprehensive Analysis of Machine Learning and Deep Learning Based Product Recommendation System
    IC4S
    Springer
    DOI: 10.1007/978-3-031-77075-3_25
GKalyan Chakravarthi1, Raghvendra Kumar1,*, Ssvr Kumar Addagarla2
  • 1: Department of Computer Science and Engineering, School of Engineering and Technology, GIET University
  • 2: Department of Computer Science and Engineering (AI and ML)
*Contact email: raghvendra@giet.edu

Abstract

Recommender Systems (RS) have been widely applied in various real-time applications to support identifying valuable information. The RS tries to give actual suggestions to every user based on their behavior as well as interests. Recommendations generated by these systems often contend with the unique personal interests of individual users, thereby playing a pivotal role in their decision-making processes. Recommender Systems (RS) act as efficient tools for filtering vast amounts of online data, shaping the behaviors of smartphone users, personalization trends, and the evolution of internet access. RS are generally classified into three primary types: Hybrid Filtering, Content-Based Filtering (CBF), and Collaborative Filtering (CF). These systems find wide application across various fields including online education systems, e-commerce, marketing, tourism, food service, movies, business, and beyond. Although the recent RS are well-known in providing valuable recommendations, they suffer from a number of limitations as well as challenges such as scalability, sparsity, and cold-start and so on. Due to the various approach’s existence, the selection of these approaches becomes challenging during the development of application-based RS. Moreover, every approach comes with its individual feature sets, advantages as well as limitations, which must be addressed. This survey reviews the research inclinations which integrates the progressive technical characteristics of the RS. The aim of this survey is to ensure a systematic review on recent contributions in RS domain, and concentrated on various applications such as education, food, products and so on. This survey provides a comprehensive review of these types of RS, recent literature, and applications of visual RS.

Keywords
Content based Filtering Decision-making process Hybrid Filtering Collaborative Filtering and Recommendation Systems
Published
2025-02-09
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-77075-3_25
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