[1]SHEN Miao,LAI Tianping,WANG Sumei,et al.Design and implementation of the course recommendation engine in Peking University[J].CAAI Transactions on Intelligent Systems,2015,10(3):369-375.[doi:10.3969/j.issn.1673-4785.201409045]
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Design and implementation of the course recommendation engine in Peking University

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 369-375 Column: 学术论文—智能系统 Public date: 2015-06-25
Title:
Design and implementation of the course recommendation engine in Peking University
Author(s):
SHEN Miao;  LAI Tianping;  WANG Sumei;  PENG Yiming;  GAO Zhitong
Computer Center, Peking University, Beijing 100871, China
Keywords:
Peking University; course recommendation; recommendation engine; collaborative filtering; elective system; management information systems
CLC:
TP319
DOI:
10.3969/j.issn.1673-4785.201409045
Abstract:
In order to provide a more personalized and humanized service for students and teachers in the management information system, a recommendation engine is applied to the Peking University student course-selecting system. The Peking University course recommendation engine is designed and implemented. The improved recommendation algorithm is a collaborative filtering algorithm on the premises of student attribute classification. The recommendation is achieved through analyzing different points between course recommendation and commercial recommendation, analyzing student attribute and improving the calculation method of students’ similarity. As the platform, course-selecting system in Peking University recommend courses to 10 682 undergraduate students in the first semester of the 2013-2014 school year. The precision of recommendation results is 34.6%. This system can provide the effective guidance for the students choosing courses, and fill the blank of the system’s intelligent and personalized.
References:
[1] 赵晨婷, 马春娥. 探索推荐引擎内部的秘密, 第1部分:推荐引擎初探.[EB/OL]． [2014-09-25]. http://www.ibm. com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html.
[2] 张瑶, 陈维斌, 傅顺开. 基于大数据的高校图书馆推荐系统仿真研究[J]. 计算机工程与设计, 2013, 34(7): 2533-2541. ZHANG Yao, CHEN Weibin, FU Shunkai. Simulation study of recommendation system for university library based on big data[J]. Computer Engineering and Design, 2013, 34(7): 2533-2541.
[3] 祁褎然, 潘志成, 罗敬, 等. 大学选课推荐系统的数学模型[J]. 南开大学学报:自然科学版, 2011, 44(4): 50-55.QI Youran, PAN Zhicheng, LUO Jing, et al. The mathematical model of the university course recommendation system[J]. Acta Scientiarum Naturalium Universitatis Nankaiensis, 2011, 44(4): 50-55.
[4] 陆刚. 数字信息资源利用中的“马太效应”及消除策略[J]. 图书馆建设, 2003(6): 43-45. LU Gang. The Matthew effect of the digital information resources using in the library and its solutions[J]. Library Development, 2003(6): 43-45.
[5] GOLDBERG D, NICHOLS D, OKI B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992, 35(12): 61-70.
[6] RESNICK P, IACOVOU N, SUCHAK M, et al. Grouplens: an open architecture for collaborative filtering of netnews[C]//Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work. New York, USA, 1994: 175-186.
[7] 项亮. 推荐系统实践[M]. 北京: 人民邮电出版社, 2012.
[8] GE M Z, DELGADO-BATTENFELD C, JANNACH D. Beyond accuracy: evaluating recommender systems by coverage and serendipity[C]//Proceedings of the 4th ACM Conference on Recommender Systems. New York, USA, 2010: 257-260.
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