Software Engineering

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The Teaching Resources Recommendation System Research Based on Java EE

Received: 20 April 2016    Accepted:     Published: 21 April 2016
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Abstract

With the growing popularity of teaching resources platform construction, various forms of teaching resource materials more and more, how to provide users with intelligent recommendation of teaching resources is a key issue. This article will be introduced to the trust relationship neighbor recommended an improved algorithm recommendation algorithm, designed to ease cold start recommendation system, we recommend that affect the process of distrust. The recommendation that the improved algorithm is applied to Java EE-based teaching resource recommendation system, effectively improve the system of teaching resources recommended to the user efficiency and accuracy, improve the real-time requirements so that online learning system.

DOI 10.11648/j.se.20160402.13
Published in Software Engineering (Volume 4, Issue 2, March 2016)
Page(s) 19-26
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Trusts, Recommended Neighbor Algorithm, Java EE

References
[1] Z. Chi, C. Gang, H. M. Wang, The recommended model based on hybrid recommendation technology, [J], Computer Engineering, 2010, pp. 248-253.
[2] L. N. Yang, K. C. Liu, H. M. Wang, Learning resources personalized recommendation research oriented to virtual learning community, [J], Electrochemical education research, 2011, pp. 67-71.
[3] Robert Armstrong, Dayne Freitag, Thorsten Joachims, etc. WebWatcher: A Learning Apprentice for the World Wide Web, [J], In Working Notes of the AAAI Spring Symposium: Information Gathering form Heterogeneous, Distributed Environments, 1995, pp. 6-12.
[4] JCP Expert Group. JSR-244 (Java EE. O) [S/OL]. http://jcp.org/en/jsr/detail?id=244.
[5] T. C. Wang, The design and implementation of online examination system based on Java EE, [D], South China University of Technology, 2014(5), pp. 6-8.
[6] G. Li, Classic Java EE enterprise application of actual combat: Based on the integration of WebLogic/JBoss JSF+EJB 3+JPA. development Beijing: Publishing House of electronics industry, 2010, pp. 13.
[7] H. M. Li, EJB3. 0 Introduction to the classic, [M], Tsinghua university press, 2009.
[8] J. Cu, Based on the radio and television university comprehensive practice link of Java EE, [J] Jilin university, 2011, pp. 16-18.
[9] Q. Xue, The design and implementation of online bookstore based on MVC mode and Java EE Technology, [J], Micro computer application, 2014(8), pp. 37
[10] M. P. Fu, Based on the JSF and EJB3. 0 Web application research, [D], Chengdu university of technology, 2013, pp. 14-16.
[11] P. Gao, Recommended System Research and Applications Information similarity, [D], Shanghai Jiaotong University, 2012, pp. 28.
[12] F. Bai, Improved K nearest neighbor algorithm in text classification page, [D], Anhui University, 2010, pp. 6.
[13] G. Y, Cai, Improvement of personalized recommendation algorithm in collaborative filtering, [D], Jilin University, 2013, pp. 15.
[14] W. X. Hu, N. Z. Cheng, Study on the calculation model of the degree of trust in electronic commerce transactions, [J], Electronic Commerce, 2007, pp. 61.
[15] K. Lu, L. Xie, M. C. lI, A fusion implicitly trusted collaborative filtering recommendation algorithm, [J], Small and micro computer system, 2016, pp. 242-243.
[16] Q. Zhuang, Research on low complexity coverage algorithm for Wireless Sensor Networks, [J], Electronic world, 2015(8), pp. 82.
[17] Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. kantor, Recommendation system technology, evaluation and efficient algorithm,[M], Machinery Industry Press, 2015, pp. 89-90.
[18] D. T. Sun, T. He, F. H. Zhang, Hybrid recommendation algorithm for cold start in recommendation system, [J], Computers and modernization, 2012, pp. 59-62.
[19] Y. N. Niu, Design and implementation of personalized recommendation system of teaching resources, [D], Dalian University of Technology, 2013, pp. 57-59.
[20] L. L. Yang, Personalized researching the construction of network teaching resources system, [C], Xinjiang normal university, 2012, pp. 24-25.
[21] Q. Zheng, Based on the JSF and EJB3. 0 Web application research, [C], Chengdu university of technology, 2013, pp. 35.
[22] M. Y. Yuan, H. R. Wang, Java EE development enterprise programming example explanation, [M], Tsinghua university press, 2013, pp. 39-391.
[23] B. Song, Java Web application and development of the tutorial, [M], Tsinghua university press, 2006, pp. 80-186.
Author Information
  • College of Software, Shenyang Normal University, Liaoning Shenyang, China

  • College of Software, Shenyang Normal University, Liaoning Shenyang, China

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  • APA Style

    Haihui Wu, Bo Song. (2016). The Teaching Resources Recommendation System Research Based on Java EE. Software Engineering, 4(2), 19-26. https://doi.org/10.11648/j.se.20160402.13

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    ACS Style

    Haihui Wu; Bo Song. The Teaching Resources Recommendation System Research Based on Java EE. Softw. Eng. 2016, 4(2), 19-26. doi: 10.11648/j.se.20160402.13

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    AMA Style

    Haihui Wu, Bo Song. The Teaching Resources Recommendation System Research Based on Java EE. Softw Eng. 2016;4(2):19-26. doi: 10.11648/j.se.20160402.13

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  • @article{10.11648/j.se.20160402.13,
      author = {Haihui Wu and Bo Song},
      title = {The Teaching Resources Recommendation System Research Based on Java EE},
      journal = {Software Engineering},
      volume = {4},
      number = {2},
      pages = {19-26},
      doi = {10.11648/j.se.20160402.13},
      url = {https://doi.org/10.11648/j.se.20160402.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.se.20160402.13},
      abstract = {With the growing popularity of teaching resources platform construction, various forms of teaching resource materials more and more, how to provide users with intelligent recommendation of teaching resources is a key issue. This article will be introduced to the trust relationship neighbor recommended an improved algorithm recommendation algorithm, designed to ease cold start recommendation system, we recommend that affect the process of distrust. The recommendation that the improved algorithm is applied to Java EE-based teaching resource recommendation system, effectively improve the system of teaching resources recommended to the user efficiency and accuracy, improve the real-time requirements so that online learning system.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - The Teaching Resources Recommendation System Research Based on Java EE
    AU  - Haihui Wu
    AU  - Bo Song
    Y1  - 2016/04/21
    PY  - 2016
    N1  - https://doi.org/10.11648/j.se.20160402.13
    DO  - 10.11648/j.se.20160402.13
    T2  - Software Engineering
    JF  - Software Engineering
    JO  - Software Engineering
    SP  - 19
    EP  - 26
    PB  - Science Publishing Group
    SN  - 2376-8037
    UR  - https://doi.org/10.11648/j.se.20160402.13
    AB  - With the growing popularity of teaching resources platform construction, various forms of teaching resource materials more and more, how to provide users with intelligent recommendation of teaching resources is a key issue. This article will be introduced to the trust relationship neighbor recommended an improved algorithm recommendation algorithm, designed to ease cold start recommendation system, we recommend that affect the process of distrust. The recommendation that the improved algorithm is applied to Java EE-based teaching resource recommendation system, effectively improve the system of teaching resources recommended to the user efficiency and accuracy, improve the real-time requirements so that online learning system.
    VL  - 4
    IS  - 2
    ER  - 

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