Abstract
This paper mainly studies personalized recommendation system of web academic information based on big data and quality monitoring technology. Starting with big data technology and information quality monitoring, take web academic information recommendation as the application background, conduct in-depth research on information quality monitoring methods, build an information quality monitoring system suitable for the information recommendation system, and provide users with high-quality web academic information recommendation services.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
China Internet Network Information Center. http://www.cnnic.net.cn/
Resnick, P., Iakovou, N., Sushak, M., et al.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of 1994 Computer Supported Cooperative Work Conference, Chapel Hill, pp. 175–186 (1994)
Jun, W.: The Intelligence Age, pp. 3–71. China Citicpress, Beijing (2016)
Chen, L.-C., Kuo, P.-J., Liao, I.-E.: Ontology-based library recommender system using MapReduce. Clust. Comput. 18(1), 113–121 (2015)
Jin, Z.: Design and implementation of big data-based educational resources personalized recommendation system. University of the Chinese Academy of Sciences, Beijing (2015)
Ballou, D.P., Pazer, H.L.: Modelling data and process quality in multi-input, multi-output information system. Manag. Sci. 31(2), 150–162 (1985)
Richard, Y.W., Reddy, M.P., Henry, B.K.: Toward quality data: an attribute-based approach. Decis. Support Syst. 13, 349–372 (1995)
Yang, W.L., Diane, M.S.: A methodology for information quality assessment. Inf. Manag. 40, 133–146 (2002)
Yang, W.L.: Context-reflective data quality problem solving. J. Manag. Inf. Syst. 20(3), 93–119 (2003)
Martinez-Cruz, C., Porcel, C., Bernabe-Moreno, J., Herrera-Viedma, E.: A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Inf. Sci. 311, 102–118 (2015)
Li, S., Xu, X., Xu, M.: The measures of books’ recommending quality and personalized book recommendation service based on bipartite network of readers and books’ lending relationship. J. Libr. Sci. China 38(205), 83–95 (2013)
Zhou, T.: Top ten challenges for personalized recommendations. Commun. CCF 8(7), 48–61 (2012)
Sarwar, B., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: International Conference on Computer and Information Science, pp. 27–28 (2002)
Chu, X., Cai, F., Cui, C., Hu, M., Li, L., Qin, Q.: Adaptive recommendation model using meta-learning for population-based algorithms. Inf. Sci. 192–210 (2019)
Wang, D., Liang, Y., Xu, D., Feng, X., Guan, R.: A content-based recommender system for computer science publications. Knowl. Based Syst. 1–7 (2019)
Zha, X., Zhang, J., Yan, Y.: Impacting factors of users’ academic information seeking behavior in the context of microblogs: a dual-route perspective of information quality and information source credibility. J. Libr. Sci. China 41(217), 71–86 (2015)
Kalczynski, P.J., Chou, A.: Temporal document retrieval model for business news archives. Inf. Process. Manag. 41(3), 635–650 (2005)
Jiang, Z.: Chronological citation recommendation technology based on information-need shifting. Dalian Maritime University, Dalian (2015)
Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J.R., Zhang, Y.-C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. 107(10), 4511–4515 (2010)
Shi, C., Kaminsky, M., Gbbons, P.B., Xiao, F.:Dsybil: optimal sybil-resistance for recommendation systems. In: Proceeding of the 30th IEEE Symposium on Security and Privacy, pp. 281–298. IEEE Press (2009)
Acknowledgment
This work is supported by the New engineering development mode of information technology for new finance – takes Hebei University of Economics and Trade as an example (E-JSJRJ20201310) and Information Technology Theory and Practice Teaching Research Supporting the New Finance Education Reform- -Take Hebei University of Economics and Trade as an example (2020GJJG140). Scientific Research Fund Project of Hebei University of Economic and Trade (2020PY10).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sun, J., Zhao, Y., Liu, P., Li, J., Zhai, H.W. (2021). Personalized Recommendation System of Web Academic Information Based on Big Data and Quality Monitoring Technology. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_28
Download citation
DOI: https://doi.org/10.1007/978-981-16-7502-7_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7501-0
Online ISBN: 978-981-16-7502-7
eBook Packages: Computer ScienceComputer Science (R0)