Abstract
The rapid development of the Internet has greatly facilitated people’s work and life, but at the same time, the information overload has also made people feel at a loss in the face of mixed data. The search engine and recommendation system created for this can help users filter and filter what they need. Information, but as people’s requirements for personalized services are getting higher and higher, traditional recommendation methods cannot meet more precise needs. Therefore, this article comprehensively explains the new recommendation system that introduces the concept of the Semantic Web, and introduces its origin, implementation, and finally, the successful application of semantic recommendation system in four different fields is cited.
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References
Spicer, M.P.: Following the Herd: An Economic Analysis of the Effects of Herd Mentality on the US Housing Bubble (2011)
Anderson, C., Andersson, M.P.: Long tail (2004)
Das, D., Sahoo, L., Datta, S.: A survey on recommendation system. International J. Comput. Appl. 160(7), 6–10 (2017)
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 253–260, August 2002
Hess, T.: Recommender Engines Seminar Paper, 1 February 2009
Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Davies, J., Fensel, D., Van Harmelen, F.: Towards the Semantic Web. Wiley, Chichester (2003)
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 34–43 (2001)
Wanaskar, U., Vij, S., Mukhopadhyay, D.: A hybrid web recommendation system based on the improved association rule mining algorithm. arXiv preprint arXiv:1311.7204 (2013)
Chang, L., Zhang, W.T., Gu, T.L., Sun, W.P., Bin, C.Z.: Review of recommendation systems based on knowledge graph. CAAI Trans. Intell. Syst. 14(2), 207–216 (2019)
de Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-aware content-based recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 119–159. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_4
Adrian, B., Sauermann, L., Roth-Berghofer, T.: Contag: a semantic tag recommendation system. Proc. I-Semant. 7, 297–304 (2007)
Bizer, C.: The emerging web of linked data. IEEE Intell. Syst. 24(5), 87–92 (2009)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52
IJntema, W., Goossen, F., Frasincar, F., Hogenboom, F.: Ontology-based news recommendation. In: Proceedings of the 2010 EDBT/ICDT Workshops, pp. 1–6, March 2010
Lu, J., Shambour, Q., Xu, Y., Lin, Q., Zhang, G.: BizSeeker: a hybrid semantic recommendation system for personalized government-to-business e-services. Internet Res. Electron. Netw. Appl. Policy 20(3), 342–365 (2010)
Shishehchi, S., Banihashem, S.Y., Zin, N.A.M.: A proposed semantic recommendation system for e-learning: a rule and ontology based e-learning recommendation system. In: 2010 International Symposium on Information Technology, vol. 1, pp. 1–5. IEEE, June 2010
Kushwaha, N., Goyal, R., Goel, P., Singla, S., Vyas, O.P.: LOD cloud mining for prognosis model (Case study: Native app for drug recommender system). In: Advances in Internet of Things (2014)
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Chen, W. (2022). Recommendation System Based on Semantic Web. In: Macintyre, J., Zhao, J., Ma, X. (eds) The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIoT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 98 . Springer, Cham. https://doi.org/10.1007/978-3-030-89511-2_66
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