Enhancing Recommender Systems: A Hybrid Approach for Precision Matchmaking in Digital Environments

Authors

  • Nilesh Rathod Department of Artificial Intelligence and Machine Learning, Faculty of Recommendation Systems, Shri Dwarkadas J. Sanghvi College of Engineering, Mumbai, India
  • Aruna Gawade Department of Artificial Intelligence and Machine Learning, Faculty of Machine Learning, Shri Dwarkadas J. Sanghvi College of Engineering, Mumbai, India
  • Komal Patil Department of Artificial Intelligence and Machine Learning, Faculty of Deep Learning, Shri Dwarkadas J. Sanghvi College of Engineering, Mumbai, India
  • Vidit Parekh Department of Artificial Intelligence and Machine Learning, Student of AIML, Shri Dwarkadas J. Sanghvi College of Engineering, Mumbai, India
  • Yash Shah Department of Artificial Intelligence and Machine Learning, Student of AIML, Shri Dwarkadas J. Sanghvi College of Engineering, Mumbai, India
  • Shubh Shah Department of Artificial Intelligence and Machine Learning, Student of AIML, Shri Dwarkadas J. Sanghvi College of Engineering, Mumbai, India

Keywords:

Matrimonial websites, Recommendation systems, Hybrid-based Recommendation System, Intelligent Matchmaker, Algorithm & Data analysis Personalized Matches

Abstract

In a contemporary landscape where technology seamlessly integrates into every facet of people’s lives, the quest for love has also embraced the digital revolution. Matrimonial websites have emerged as pivotal agents in reshaping the online pursuit of love, with recommendation systems standing at the forefront of this trans-formative shift. The recommendation system in matrimonial websites is just that – an intelligent matchmaker powered by algorithms and data analysis. This paper introduces users to the world of recommendation systems in matrimonial websites. This paper explores how these systems work, from creating user profiles to understanding the user's preferences to offer personalized matches. This paper touch upon the technology behind the scenes, like collaborative filtering and machine learning, that makes these systems so effective. This paper highlights various aspects of data analysis and machine learning, including rating distributions, model optimization, customer sentiment analysis, user-item interaction matrices, and user engagement metrics. These insights contribute to informed decision-making, such as identifying areas for product improvement, optimizing algorithm parameters, and understanding user behaviour patterns. The technical findings underscore the importance of data-driven strategies in enhancing system performance and user experience. The paper also highlights the importance of feedback loops and real-time adjustments to ensure the recommendations are as accurate as possible.

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References

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Published

23.02.2024

How to Cite

Rathod, N. ., Gawade, A. ., Patil, K. ., Parekh, V. ., Shah, Y. ., & Shah, S. . (2024). Enhancing Recommender Systems: A Hybrid Approach for Precision Matchmaking in Digital Environments. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 571 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4895

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

License

Copyright (c) 2024 Nilesh Rathod, Aruna Gawade, Komal Patil, Vidit Parekh, Yash Shah, Shubh Shah

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