A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects

Seth, Rakhi; Sharaff, Aakanksha

doi:10.1002/9781119792529.ch3

Data Mining and Machine Learning Applications 2022

DOI: 10.1002/9781119792529.ch3

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A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects

Rakhi Seth¹,

Aakanksha Sharaff²

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Cited by 6 publications

(1 citation statement)

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“…This type of system also has the advantage of scalability. As new users join the system, the recommendation algorithm can be updated without requiring additional user data to be shared [71]. This makes the system more efficient and effective and allows for more users to be accommodated.…”

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A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.

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A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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A personalized cross-domain recommendation with federated meta learning

Zhao,

Jin,

Ren

et al. 2024

Multimed Tools Appl

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Improving recommendations utilizing users’ demographic information

Dey,

Dutta Pramanik,

Singh

et al. 2024

Qual Quant

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A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects

Cited by 6 publications

References 45 publications

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

A personalized cross-domain recommendation with federated meta learning

Improving recommendations utilizing users’ demographic information

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