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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Advancements in Hybrid Recommendation Systems |
| Authors Name: | Harshit Srivastava , Keshav Kumar Jha , Vikas Arya |
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IJRTI_202382
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| Published Paper Id: | IJRTI2504117 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | Recommendation systems have become indispensable tools for navigating the vast digital landscape, enhancing user experience by personalizing content and product suggestions. While foundational techniques like Collaborative Filtering (CF) and Content-Based Filtering (CBF) have been influential, they exhibit inherent weaknesses such as data sparsity, the cold-start problem, and limited recommendation diversity. Hybrid Recommendation Systems (HRS) represent a significant advancement, strategically combining multiple recommendation approaches to overcome these limitations and improve overall performance. This paper presents a comprehensive review of the key advancements in HRS. We trace the evolution from foundational hybridization strategies to the integration of sophisticated machine learning techniques, including Matrix Factorization and various Deep Learning architectures (e.g., NCF, RNNs, GNNs). The paper further explores the crucial role of incorporating external knowledge through Knowledge Graphs (KGs) and leveraging contextual information for more relevant and timely suggestions. Finally, we examine contemporary challenges and future research directions, encompassing explainability, fairness, scalability, cross-domain applications, and the critical need for evaluation metrics that capture aspects beyond predictive accuracy, such as novelty and diversity. This work synthesizes findings from numerous academic studies to provide a cohesive understanding of the state-of-the-art and trajectory of hybrid recommendation systems. |
| Keywords: | Hybrid Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Deep Learning, Knowledge Graphs, Matrix Factorization, Recommender Systems, Cold Start, Data Sparsity, Context-Awareness, Explainability, Fairness, Evaluation Metrics. |
| Cite Article: | "Advancements in Hybrid Recommendation Systems", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b131-b137, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504117.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2504117
Registration ID:202382
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: b131-b137 Country: New Delhi, Delhi, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504117 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504117 |
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ISSN: 2456-3315
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