Authors : Abhinav Sharma; Preksha Agrawal; Surendra Kumar Keshari

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/mry7jcz4

Scribd : https://tinyurl.com/yc2329ny

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR1249

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the realm of e-commerce, recommendation systems play a pivotal role in guiding users towards relevant products. However, existing systems often grapple with inefficiencies in handling large datasets and fail to deliver personalized recommendations tailored to individual preferences. Addressing these challenges, the study introduces an innovative approach leveraging graph databases to enhance the performance of e- commerce recommendation systems. Through comprehensive analysis, the study delves into four critical aspects: database comparison, user exploration frequency across product categories, the diversity of available category types, and user browsing history analysis. This investigation unveils Neo4j's superior efficiency over MySQL in managing extensive datasets, laying the groundwork for more robust recommendation engines. By scrutinizing user behaviour patterns, the recommender system predicts preferences with precision, promising a tailored and gratifying shopping experience for users. Moreover, with support for a diverse array of category types, users gain flexibility in exploring products based on varied criteria, addressing a crucial need in the market for personalized shopping experiences. Leveraging insights gleaned from user browsing history, the system delivers refined recommendations, poised to elevate user satisfaction and engagement within the competitive landscape of e- commerce. In conclusion, the study highlights the significance of recommendation systems in enhancing the e- commerce experience. By leveraging graph databases, particularly Neo4j, over traditional systems like MySQL, significant improvements in managing extensive datasets are demonstrated.

Keywords : E-commerce, Recommendation Systems, Neo4j, Personalized Recommendations, User Engagement, Data Analysis.

References :

1. Charles Ntumba, Samuel Aguayo, Kamau Maina "Revolutionizing Retail: A Mini Review of E-commerce Evolution" (2023).
2. Devi Sunuwar; Monika Singh " Comparative Analysis of Relational and Graph Databases for Data Provenance: Performance, Queries, and Security Considerations " (2023).
3. Pablo Sánchez , Alejandro Bellogín " Point-of-Interest Recommender Systems Based on Location-Based Social Networks: A Survey from an Experimental Perspective" (2022).
4. Breese, John S., David Heckerman, and Carl Kadie. "Empirical analysis of predictive algorithms for collaborative filtering." arXiv preprint arXiv:1301.7363 (2013).
5. Sarwar, B. M., Karypis, G., Konstan, J., & Riedl, J. (2002). Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Paper presented at the Proceedings of the fifth international conference on com- puter and information technolog.
6. D. Clark, IBM and Twitter forge partnership in data analytics, Wall Street J. Retrieved April 15, 2016 from http://www.marketwatch.com/story/ibm-andtwitter-forge- partnership-on-data-analytics-2014-10-29.
7. H.J. Watson, O. Marjanovic, Big data: the fourth data management generation, Bus. Intelligence J. 18(3) (2013) 4–8 (Chicago)
8. E. Dumbill, Making sense of big data, Big Data 1 (1) (2013)1–2.
9. M. Anshari, Y. Alas, N. Yunus, N.I. Sabtu, M.H. Hamid, Social customer relationship management and student empowerment in online learning systems, Int. J. Electronic Customer Relat. Manage. 9(2/3) (2015) 104–121.
10. H. Hinchcliffe, The state of Web 2.0, 2006. Retrieved 12^thMay,2012;from http://web2.socialcomputingmagazine.com/the_state_of_web_2 0.htm
11. P. Greenberg, CRM at the Speed of Light: Social CRM 2.0 Strategies, Tools, and Techniques for Engaging yOur Customers, fourth ed., McGraw-Hill Osborne Media, 2009.
12. De Gemmis, M., Lops, P., Semeraro, G., & Basile, P. (2008). Integrating tags in a semantic content-based recommender. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 163–170). ACM.
13. Gedikli, F., & Jannach, D. (2013). Improving recommendation accuracy based on item-specific tag preferences. ACM Transactions on Intelligent Systems and Technology (TIST), 4, 11.
14. Liu, J., Wang, W., Chen, Z., Du, X., & Qi, Q. (2012). A
15. novel user-based collaborative filtering method by inferring tag ratings. ACM SIGAPP Applied Computing Review, 12 , 48–57
16. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recom- mender systems. Computer, 42(8), 30–37.
17. Enrich, M., Braunhofer, M., & Ricci, F. (2013). Cold-start management with cross-domain collaborative filtering and tags. In International Conference on Electronic Commerce and Web Technologies (pp. 101–112). Springer.
18. Bao, T., Ge, Y., Chen, E., Xiong, H., & Tian, J. (2012).
19. Collaborative filtering with user ratings and tags. In Proceedings of the 1st International Workshop on Context Discovery and Data Mining (p. 1). ACM.
20. Wang, Z., & He, L. (2016). User identification for enhancing ip-tv recommendation. Knowledge-Based Systems, 98, 68–75.