Nikhat khan | Multimodal Recommender Systems | Best Researcher Award

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Ms. Nikhat khan | Multimodal Recommender Systems | Best Researcher Award

Research scholar at National Institute of Technology, Raipur, India

Ms. Nikhat Khan is a research scholar at NIT Raipur, specializing in Multimodal Recommender Systems, artificial intelligence, and data analysis. With a strong academic background and practical expertise, she has contributed to cutting-edge research in her field, publishing in high-impact journals and working on innovative projects. Ms. Khan is committed to advancing AI-driven systems and data analysis methods to address real-world challenges.

Publication Profile

Orcid

Google Scholar

Educational Details

Ms. Nikhat Khan is a dedicated research scholar in Computer Science and Engineering at the National Institute of Technology (NIT), Raipur, India, where she is pursuing her Ph.D. She holds a Master of Technology (M.Tech) from G.H. Raisoni Institute of Engineering and Technology, achieving a GPA of 8.5. She completed her Bachelor of Engineering (B.E.) at Sardar Patel College of Technology, establishing a strong foundation in engineering principles.

Professional Experience

Currently, Ms. Khan is deeply engaged in research focusing on Multimodal Recommender Systems. Her work has resulted in significant contributions to the academic community, including a publication in the prestigious Information Processing and Management journal. She is actively working on multiple projects in this domain, with some under review in reputed journals and others in the implementation phase. Her academic journey is marked by consistent dedication to advancing artificial intelligence and data analysis.

Research Interest

  • Multimodal Recommender Systems
  • Artificial Intelligence
  • Data Analysis

Author Metrics

  • Patents: Authored seven patents, including innovations in nitrogen assimilation, hydroponic systems, and phosphate-solubilizing fungal strains.
  • Publications and Citations: Extensive contributions to plant science, with multiple peer-reviewed articles cited in global research databases.

Publication Top Notes

Title: Exploiting Diffusion-Based Structured Learning for Item Interactions Representations in Multimodal Recommender Systems

Authors:

  • Nikhat Khan
  • D.S. Sisodia

Journal: Information Processing & Management
Volume: 62
Issue: 3
Article ID: 104075
Year: 2025

Abstract:
The paper explores innovative methods to improve item interaction representations in multimodal recommender systems by exploiting diffusion-based structured learning techniques. It investigates how this approach can enhance the ability of recommender systems to effectively model complex, multi-dimensional interactions between items across different data modalities. This research proposes novel techniques that integrate diffusion-based learning strategies, improving both the accuracy and efficiency of recommendations by learning more nuanced relationships between items and user preferences.

Conclusion

Ms. Nikhat Khan is a strong contender for the Best Researcher Award due to her innovative contributions to Multimodal Recommender Systems and AI-driven technologies. Her ability to integrate advanced techniques, such as diffusion-based learning for item interaction representations, positions her as a rising star in the AI research community.

To further strengthen her profile, expanding her industry collaborations, taking on mentorship roles, and diversifying her research applications could significantly amplify her impact. Overall, her academic achievements, innovative contributions, and commitment to AI make her an excellent candidate for this prestigious recognition.