Abstract
With the continuous increase of online courses and the rapid growth of network data, how to improve the recommendation accuracy and real-time performance of personalized recommendation system is a key issue. In order to improve the recommendation quality and real-time performance of the recommendation system, this paper uses SSM (Spring, Spring MVC, Mybatis) Framework, the most popular and latest framework of the enterprise, as the framework of the recommendation system, and uses the hybrid recommendation algorithm (Socialized Recommendation algorithm) that integrates Collaborative Filtering recommendation algorithm and social relationship to make course recommendation. In this paper, the course and relational data of scholar.com are used as data sets for research, so as to provide richer social relationships and more references for experiments.
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References
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16(3), 261–273 (2015)
Jiemin, C., Yong, T., Jianguo, L., Yibin, C.: Research on personalized recommendation algorithm. J. South China Normal Univ. 46(5), 1–8 (2014)
He, C., et al.: Scientific paper recommendation method based on academic community. J. South China Normal Univ. 44(3), 1–4 (2012)
Guo, Y., Chen, M., Wei, K.: Research of recycling resource website based on spring and MyBatis framework. In: 2015 International Conference on Information Technology and Intelligent Transportation Systems (ITITS), pp. 307–314 (2015)
Li, Q., Qing, A.: Design and implementation of student life service platform based on SSM framework. Comput. Knowl. Technol. 17(13), 80–82 (2021)
Yongchang, R., Deyi, J., Tao, X., Ping, Z.: Research on software development platform based on SSH framework structure. Procedia Eng. 15, 3078–3082 (2011)
Arthur, J., Azadegan, S.: Spring framework for rapid open source J2EE Web application development: a case study. In: Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network, pp. 90–95 (2005)
Mane, D., Chitnis, K., Ojha, N.: The spring framework: an open source java platform for developing robust Java applications. Int. J. Innovative Technol. Explor. Eng. 3, 137–143 (2013)
Shiyong, X., Tianxiang, D., Rongzheng, Z., Rongsen, W.: Research on Mybatis mapper model based on SQL template. In: 2020 International Conference on Computer Engineering and Application (ICCEA), pp. 502–505 (2020)
Węgrzynowicz, P.: Performance antipatterns of one to many association in hibernate. In: 2013 Federated Conference on Computer Science and Information Systems, pp. 1475-1481 (2013)
Hou, Y.: Design and implementation of the framework for Spring+SpringMVC+MyBatis in the development of web application. In: 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), pp. 368–371 (2017)
Li, M., Zhao, X., Yu, Y., et al.: Research progress of recommendation algorithm. Small Microcomput. Syst. 43(03), 544–554 (2022)
Chen, S., Owusu, S., Zhou, L.: Social network based recommendation systems: a short survey. In: 2013 International Conference on Social Computing, pp. 882–885 (2013)
Suhaim, A.B., Berri, J.: Context-aware recommender systems for social networks: review, challenges and opportunities. IEEE Access 9, 57440–57463 (2021)
Yiteng, P., Fazhi, H., Haiping, Y.: A social recommendation algorithm based on implicit similarity of trust relationship. Chin. J. Comput. 41(1), 65–81 (2018)
Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M., Jararweh, Y.: A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: 2018 9th International Conference on Information and Communication Systems (ICICS), pp. 102–106 (2018)
Li, W., et al.: Personalization recommendation algorithm based on trust correlation degree and matrix factorization. IEEE Access 7, 45451–45459 (2019)
Juan, Y., Zhuang, Y., Chin, W.-S., Lin, C.-J.: Field-aware factorization machines for CTR prediction. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys’16), pp. 43–50. Association for Computing Machinery, New York, NY, USA (2016)
Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on Information and knowledge management (CIKM’08). Association for Computing Machinery, New York, NY, USA, pp. 931–940 (2008)
Gasparetti, F., Sansonetti, G., Micarelli, A.: Community detection in social recommender systems: a survey. Appl. Intell. 51(6), 3975–3995 (2020). https://doi.org/10.1007/s10489-020-01962-3
Wang, M., Ma, J.: A novel recommendation approach based on users’ weighted trust relations and the rating similarities. Soft. Comput. 20(10), 3981–3990 (2015). https://doi.org/10.1007/s00500-015-1734-1
Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD’09), pp. 397–406. Association for Computing Machinery, New York, NY, USA (2009)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
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Liang, Q., Wu, Z., Lin, R., Huang, L. (2022). Course Recommendation System Based on SSM Framework. In: Zu, Q., Tang, Y., Mladenovic, V., Naseer, A., Wan, J. (eds) Human Centered Computing. HCC 2021. Lecture Notes in Computer Science, vol 13795. Springer, Cham. https://doi.org/10.1007/978-3-031-23741-6_9
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