收稿日期: 2013-10-22
修回日期: 2013-11-08
网络出版日期: 2013-11-20
基金资助
本文系国家社会科学基金重大项目“基于语义的馆藏资源深度聚合与可视化展示研究”(项目编号:11&ZD152)研究成果之一。
Research on the Collaborative Recommendation System of University Library Resources
Received date: 2013-10-22
Revised date: 2013-11-08
Online published: 2013-11-20
邱均平 , 张聪 . 高校图书馆馆藏资源协同推荐系统研究[J]. 图书情报工作, 2013 , 57(22) : 132 -137 . DOI: 10.7536/j.issn.0252-3116.2013.22.021
This paper takes Wuhan University as the practical research environment. Arming at the particularity of collaborative recommendation in university libraries and limited user rating data on library resources, it calculates users' preference value mainly in accordance with their loaning time and adjusts the value with assist of users' rating data. Users' information needs are divided into two categories of long-term information needs and short-term information needs. Then two kinds of corresponding preference value are calculated separately to get more accurate recommendation. Finally, by virtue of Apache Mahout, a collaborative filtering recommendation system is constructed to recommend books to meet two kinds of information needs, and the results are tested by email survey.
[1] Schafer J B, Frankowski D, Herlocker J, et al. Collaborative filtering recommender systems[M]//The Adaptive Web.Berlin Heidelberg:Springer Verlag, 2007: 291-324.
[2] 奉国和, 梁晓婷. 协同过滤推荐研究综述[J]. 图书情报工作, 2011, 55(16): 126-130.
[3] 马丽. 基于群体兴趣偏向度的数字图书馆协同过滤技术研究[J]. 现代图书情报技术, 2007(10):19-22.
[4] 刘飞飞. 基于多目标进化双聚类的数字图书馆协同过滤推荐系统[J]. 图书情报工作, 2011, 55(7):111-113.
[5] 董坤. 基于协同过滤算法的高校图书馆图书推荐系统研究[J]. 现代图书情报技术, 2011(11):44-47.
[6] 武建伟, 俞晓红, 陈文清. 基于密度的动态协同过滤图书推荐算法[J]. 计算机应用研究, 2010, 27(8):3013-3015.
[7] 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.
[8] 中国科教评价网: 2013-2014年综合竞争力排行榜——重点大学竞争力排行榜[EB/OL].[2013-08-07]. http://www.nseac.com/eva/CUcompkeyE.php.
[9] 刘平峰, 聂规划, 陈冬林.电子商务推荐系统研究综述[J]. 情报杂志, 2007(9):46-50.
[10] Apache Mahout[EB/OL].[2013-08-07]. http://mahout.apache.org.
[11] Linden G, Smith B, York J. Amazon. com recommendations: Item-to-item collaborative filtering[J].IEEE Internet Computing, 2003, 7(1): 76-80.
[12] Deshpande M, Karypis G. Item-based top-n recommendation algorithms[J]. ACM Transactions on Information Systems (TOIS), 2004, 22(1): 143-177.
[13] Anil R, Dunning T, Friedman E. Mahout In Action[M].Connecticut Greenwich:Manning Publications, 2011:48-55.
[14] Willmott C J, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance[J]. Climate Research, 2005, 30(1): 79-82.
[15] 孙小华. 协同过滤系统的稀疏性与冷启动问题研究[D]. 杭州:浙江大学, 2005.
/
| 〈 |
|
〉 |