Analysis of Recommender Systems in Heterogeneous Information Networks using HINPy

Authors

  • 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.

Keywords:

Heterogeneous Information Networks, Python workbench, HINPy, recommender systems

Abstract

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

Downloads

Download data is not yet available.

References

Sun, Yizhou, and Jiawei Han. "Mining heterogeneous information networks: a structural analysis approach." Acm Sigkdd Explorations Newsletter 14.2 (2013): 20-28.

Liu, Jiawei, et al. "A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources."AI Open 3 (2022): 40-57.

Sun, Yizhou & Han, Jiawei & Aggarwal, Charu & Chawla, Nitesh. (2012). When Will It Happen? — Relationship Prediction in Heterogeneous Information Networks. WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 663-672. 10.1145/2124295.2124373.

Yizhou Sun, Yintao Yu, Jiawei Han, "Ranking-based clustering of heterogeneous information networks with star network schema ",Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009), pp. 797-806.

Yizhou Sun, Jiawei Han, Xifeng Yan, S Yu Philip, Tianyi Wu,"Pathsim: meta path-based top-k similarity search in heterogeneous information networks",Proceedings of the VLDB Endowment, 4 (2011), pp. 992-1003.

Lao Ni, William W. Cohen,"Relational retrieval using a combination of path-constrained random walks",Mach. Learn., 81 (1) (2010), pp. 53-67

Jing Zhang, Jie Tang, Bangyong Liang, Zi Yang, Sijie Wang, Jingjing Zuo, Juanzi Li,”Recommendation over a heterogeneous social network “,2008 the Ninth International Conference on Web-Age Information Management, IEEE (2008), pp. 309-316.

Pierre De Handschutter, Nicolas Gillis, Xavier Siebert,“A survey on deep matrix factorizations”,Computer Science Review,2021,Volume 42.

Chuan Shi, Xiangnan Kong, Yue Huang, S Yu Philip, Bin Wu”Hetesim: a general framework for relevance measure in heterogeneous networks”,IEEE Trans. Knowl. Data Eng., 26 (10) (2014), pp. 2479-2492.

Xiaotian Han, Chuan Shi, Senzhang Wang, S Yu Philip, Li Song “Aspect-level deep collaborative filtering via heterogeneous information networks”,IJCAI (2018), pp. 3393-3399

Huan Zhao, Yingqi Zhou, Yangqiu Song, Dik Lun Lee, “Motif enhanced recommendation over heterogeneous information network”,Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019), pp. 2189-2192

https://archive.ics.uci.edu/dataset/232/restaurant+consumer+data

Sadhana Kodali, Madhavi Dabbiru, B Thirumala Rao ,"A Cuisine Based Recommender System Using k-NN And Mapreduce Approach",International Journal of Innovative Technology and Exploring Engineering (IJITEE) , Volume-8 Issue-7 May, 2019

Downloads

Published

23.02.2024

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 https://ijisae.org/index.php/IJISAE/article/view/4860

Issue

Section

Research Article