Abstract
The problem of recommending similar sets of items in the online business community is called item recommendation. An item recommendation aims to recommend a new item that matches the user’s interests. Universally, recommendation amenities have become significant due to their support in e-commerce applications like online shopping, digital promotions, and various research domains. The collaborative approach of the recommender engine filters out the k-nearest neighbours and then the similarity is compared between the neighbourhoods. In this paper, an algorithm is proposed, to recommend the items to the users with respect to the uniqueness of users’ choice. The result achieved is a mixture of both types of items, which are commonly and rarely bought by others. The proposed technique uses machine learning modules to learn actively and recommend.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yu, S., Liu, J., Yang, Z., Chen, Z., Jiang, H., Tolba, A., Xia, F.: Pave: personalized academic venue recommendation exploiting co-publication networks. J. Netw. Comput. Appl. 104, 38–47 (2018)
Trappey, A.J.C., Trappey, C.V., Wu, C.Y., Fan, C.Y., Lin, Y.L.: Intelligent patent recommendation system for innovative design collaboration. J. Netw. Comput. Appl. 36(6), 1441–1450 (2013)
Liu, Q., Zhou, M., Zhao, X.: Understanding news 2.0: a framework for explaining the number of comments from readers on online news. Inf. Manag. 52(7), 764–776 (2015)
Liu, J., Kong, X., Xia, F., Bai, X., Wang, L., Qing, Q., Lee, I.: Artificial intelligence in the 21st century. IEEE Access 6(99), 34403–34421 (2018)
Liu, H., Yang, Z., Lee, I., Xu, Z., Yu, S., Xia, F.: Car: INCORPORATING fiLTERED CITATION relations for scientific article recommendation. In: Proceedings of 2015 IEEE International Conference on Smart City/Social Com/Sustain Com (Smart City), pp. 513–518. IEEE (2015)
Bollen, J., Nelson, M.L., Geisler, G., Araujo, R.: Usage derived recommendations for a video digital library. J. Netw. Comput. Appl. 30(3), 1059–1083 (2007)
Song, T., Yi, C., Huang, J.: Whose recommendations do you follow? an investigation of tie strength, shopping stage, and deal scarcity. Inf. Manag. 54(8), 1072–1083 (2017)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: The Adaptive Web, pp. 325–341. Springer, Heidelberg (2007)
Miah, S.J., Vu, H.Q., Gammack, J., Mcgrath, M.: A big data analytics method for tourist behaviour analysis. Inf. Manag. 54(6), 771–785 (2016)
Zhang, W., Wang, J., Feng, W.: Combining latent factor model with location features for event-based group recommendation. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 910–918 (2013)
Zhao, W., Wu, R., Liu, H.: Paper recommendation based on the knowledge gap between a researcher’s background knowledge and research target. Inf. Process. Manag. 52(5), 976–988 (2016)
Balabanovi´c, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Sugiyama, K., Kan, M.-Y.: Scholarly paper recommendation via user’s recent research interests. In: Proceedings of the 10th Annual Joint Conference on Digital Libraries, pp. 29–38. ACM (2010)
Feng, H., Tian, J., Wang, H.J., Li, M.: Personalized recommendation based on time-weighted overlapping community detection. Inf. Manag. 52(7), 789–800 (2015)
Bai, X., Wang, M., Lee, I., Yang, Z., Kong, X., Xia, F.: Scientific paper recommendation: a survey. IEEE Access 7, 9324–9339 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kumar, S., Sarangi, A., Mohanty, R.P. (2021). A Hybrid Recommender System: Uniqueness of Choices by Using Machine Learning Technique. In: Durakbasa, N.M., Gençyılmaz, M.G. (eds) Digital Conversion on the Way to Industry 4.0. ISPR 2020. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-62784-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-62784-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62783-6
Online ISBN: 978-3-030-62784-3
eBook Packages: EngineeringEngineering (R0)