Abstract
On the internet, with the increased number of users, its use for recommending products and services is also increasing. But there is a need to filter that data and provides only relevant recommendations. Recommendation systems help in solving this problem by providing personalized recommendations from a large pool of data. This paper provides an overview of the recommendation system along with its various filtering techniques. The paper also discusses various challenges faced by the current recommendation systems and the possible research areas in this field that can improve its efficiency.
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References
L. Cao, Non-IID recommender systems: a review and framework of recommendation paradigm shifting. Engineering 2(2), 212–224 (2016). ISSN 2095-8099. https://doi.org/10.1016/J.ENG.2016.02.013. (https://www.sciencedirect.com/science/article/pii/S2095809916309481)
S. Gupta, A. Mishra, Evolution of online marketing tools, approaches, and strategies with associated challenges: a survey. Int. J. Technol. Diffus. (IJTD) 12(3), 61–82 (2021). https://doi.org/10.4018/IJTD.2021070104
J. Myrberg, A recommender system for an online auction (2016). https://sal.aalto.fi/publications/pdf-files/tmyr16_public.pdf
A. Rajaraman, J. Leskovec, J. Ullman, Mining of massive datasets (2014).https://doi.org/10.1017/CBO9781139058452
F. Isinkaye, Y. Folajimi, B. Ojokoh, Recommendation systems: Principles, methods and evaluation. Egypt. Inform. J. 16https://doi.org/10.1016/j.eij.2015.06.005
G. Adomavicius, A. Tuzhilin, Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99
S. Gupta, A. Mishra, Big data analytics: approaches and applications in online marketing (July 12, 2021). Available at 2SSRN: https://ssrn.com/abstract=3884656 or https://doi.org/10.2139/ssrn.3884656
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Gupta, S., Mishra, A. (2023). Recommendation Engine: Challenges and Scope. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore. https://doi.org/10.1007/978-981-19-4676-9_59
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DOI: https://doi.org/10.1007/978-981-19-4676-9_59
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