1 minute read
International for Research in Applied Science & Engineering Technology (IJRASET)
ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538
Advertisement
Volume 11 Issue I Jan 2023- Available at www.ijraset.com
VII. CONCLUSION
Recommendation systems can be very powerful tools in an enterprise's arsenal, and future developments will add even more business value. Some applications include the ability to predict seasonal purchases based on recommendations, identify key purchases, and provide customers with better recommendations that can increase retention and brand loyalty. increase. Most businesses will be able to use recommendation systems, so I encourage you to learn more about this fascinating area. The importance of recommendation systems is increasing due to information overload. Especially in content-based recommendation systems, we are trying to find new ways to improve the accuracy of movie representation.
References
[1] Ahmed, A. A., Naomie, S., (2016). “Markov chains Recommendation System (MCRS)”, in: Int. J. Nov. Res. Comput. Sci. Softw. Eng, pp. 11-26.
[2] Adomavicius, G., Tuzhilin, A., (2005). “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”, in: IEEE transactions on knowledge and data engineering, pp. 734-749.
[3] Adomavicius, G., Tuzhilin, A., (2011). “Context-aware recommender systems”, in: Recommender systems handbook, pp. 217-253.
[4] Agarwal, A., Chauhan, M., (2017). “Similarity measures used in recommender systems: a study”, in: International Journal of Engineering Technology Science and Research IJETSR, ISSN, pp. 2394-3386.
[5] Bennett, J., Lanning, S., (2007). “The Netflix prize, in: Proceedings of KDD Cup and Workshop”, no 2, pp. 51-52.
