Analysis of Recommender Systems in Heterogeneous Information Networks using HINPy


  • Sadhana Kodali Assistant Professor, Department of CSE(H), Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dt, Andhra Pradesh, India
  • Madhavi Dabbiru Professor,Dr. Lankapalli Bullayya College of Engineering ,Visakhapatnam,Andhra Pradesh,India.
  • Thirupathi Rao Komati Professor, Department of CSE, Gitam deemed to be University, Visakhapatnam, Andhra Pradesh, India.


Heterogeneous Information Networks, Python workbench, HINPy, recommender systems


Recommender systems play a pivotal role in enhancing user experiences across various online platforms, from e-commerce websites to social media and content streaming services. Traditional recommender systems have primarily relied on homogeneous data structures, limiting their ability to effectively capture complex user-item interactions. Heterogeneous Information Networks (HINs) have emerged as a powerful paradigm to address these limitations by modeling diverse types of entities and relationships present in real-world recommendation scenarios. This paper provides a comprehensive review on the usage of HINPy which is a python workbench used for the analysis of recommender systems. HINPy is also a powerful workbench for the representation of networks. This paper analyses the cuisine based recommender system using HINPy and focuses on introducing the foundational concepts of HINPy and unique advantages in capturing rich and diverse information about users, items


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How to Cite

Kodali , S. ., Dabbiru, M. ., & Komati, T. R. . (2024). Analysis of Recommender Systems in Heterogeneous Information Networks using HINPy. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 477–482. Retrieved from



Research Article