[Objective] Service recommendation of the content of traditional digital literature resources is unable to fully exploit the user potential information demand and the ratings matrixes are always sparse. This paper provides an algorithm using collaborative filtering algorithm and association semantic link. [Methods] A recommendation algorithm for the content of digital literature resources is proposed by using the association semantic link and collaborative filtering algorithm. [Results] The experimental result shows that the algorithm can overcome the problems of the potential information needs of the users and the sparsity of the matrix. [Limitations] Lack of large-scale collection of digital resources, and the experimental cases are few. [Conclusions] The algorithm can fully exploit the users' information demand and generate the literature recommendation information. Finally, the validity and practicability of the proposed algorithm are verified by experiments.
毕强, 刘健. 数字文献资源内容服务推荐方法研究[J]. 现代图书情报技术, 2015, 31(12): 21-27.
Bi Qiang, Liu Jian. Research on the Service Recommendation of the Content of Digital Literature Resources. New Technology of Library and Information Service, 2015, 31(12): 21-27.
[1] 马炎. 一种自适应的协作过滤图书推荐系统研究[J]. 情报杂志, 2008, 27(5): 105-106, 109. (Ma Yan. Research on the Adaptive Collaborative Filtering Recommendation System [J]. Journal of Information, 2008, 27(5): 105-106, 109.)
[2] 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. (Dong Kun. Research of Personalized Book Recommender System of University Library Based on Collaborative Filter [J]. New Technology of Library and Information Service, 2011(11): 44-47.)
[3] 吴志强, 马慧娟. 协同信息推荐技术及其在数字图书馆中的应用研究述评[J]. 图书情报工作, 2012, 56(19): 122-127. (Wu Zhiqiang, Ma Huijuan. Review on Researches About the Application of Collaborative Information Recommendation Technologies in Digital Libraries [J]. Library and Information Service, 2012, 55(19): 122-127.)
[4] 熊拥军. 数字图书馆个性化服务资源推荐模式分析[J]. 图书馆, 2014(2): 132-134. (Xiong Yongjun. The Model Analysis of Personalized Information Recommendation Service in Digital Library [J]. Library, 2014(2): 132-134.)
[5] Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems [J]. Computer, 2009, 42 (8): 30-37.
[6] Ghazarian S, Nematbakhsh M A. Enhancing Memory-based Collaborative Filtering for Group Recommender Systems Original Research Article [J]. Expert Systems with Applications, 2015, 42(7): 3801-3812.
[7] Zhu Z, Wang J Y. Book Recommendation Service by Improved Association Rule Mining Algorithm [C]. In: Proceedings of the 6th International Conference on Machine Learning and Cybernetics, Hong Kong, China. 2007: 19-22.
[8] Lopes G R, Souto M A M, Wives L K, et al. A Personalized Recommender System for Digital Libraries [C]. In: Proceedings of the 14th Brazilian Symposium on Multimedia and the Web, Brazil. 2008: 59-66.
[9] Amini B, Ibrahim R, Othman M S, et al. Incorporating Scholar's Background Knowledge into Recommender System for Digital Libraries [C]. In: Proceedings of the 5th Malaysian Conference in Software Engineering (MySEC). 2011: 516-523.
[10] Will T, Srinivasan A, Im I, et al. Search Personalization: Knowledge-Based Recommendation in Digital Libraries[C]. In: Proceedings of the 15th Americas Conference on Information Systems. 2009: 728-735.
[11] 杨杰. 个性化推荐系统应用及研究[D]. 合肥: 中国科学技术大学, 2009. (Yang Jie. Application and Research of Personalized Recommender Systems [D]. Hefei: University of Science and Technology of China, 2009.)
[12] 赵琴琴, 鲁凯, 王斌. SPCF 基于内存的传播式协同过滤推荐算法[J]. 计算机学报, 2013, 36(3): 671-672. (Zhao Qinqin, Lu Kai, Wang Bin. SPCF: A Memory Based Collaborative Filtering Algorithm via Propagation [J]. Chinese Journal of Computers, 2013, 36(3): 671-672.)
[13] 贾丽会, 张修如. BP算法分析与改进[J]. 计算机技术与发展, 2006, 16(10): 101-103. (Jia Lihui, Zhang Xiuru. Analysis and Improvements of BP Algorithm [J]. Computer Technology and Development, 2006, 16(10): 101-103.)
[14] Jung K Y. User Preference Through Bayesian Categorization for Recommendation [C]. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China. 2006: 112-119.
[15] 任磊. 推荐系统关键技术研究[D]. 上海: 华东师范大学, 2012. (Ren Lei. Research on Some Key Issues of Recommender Systems [D]. Shanghai: East China Normal University, 2012.)
[16] Chedrawy Z, Abidi S S R. An Adaptive Personalized Recommendation Strategy Featuring Context Sensitive Content Adaptation [C]. In: Proceedings of the 4th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. 2006: 61-70.
[17] 李静云. 基于用户情境感知的移动图书馆知识推荐系统设计[J]. 图书馆理论与实践, 2013(6): 19-21. (Li Jingyun. Design of Knowledge Recommender System Based-on Users' Context-aware for Mobile Library [J]. Library Theory and Practice, 2013(6): 19-21.)
[18] Hai Z, Zheng L. Ranking Semantic-linked Network [C]. In: Proceedings of the 12th International Conference on World Wide Web. 2003: 114-117.
[19] 徐峥. 大规模网络资源环境下关联语义链网络模型及其应用研究[D]. 上海: 上海大学, 2012. (Xu Zheng. Building Association Link Network for Managing Large Scale Web Resources [D]. Shanghai: Shanghai University, 2012.)
[20] 刘飞飞. 基于多目标优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. (Liu Feifei. Digital Library Collaborative Filtering Recommendation System Based on Multiobjective Evolutionary Biclustering [J]. Library and Information Service, 2011, 55(7): 111-113.)
[21] 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. (Ma Li. Study on Digital Library Collaborative Filtering Technology Based on Group Interest Trend Degree [J]. New Technology of Library and Information Service, 2007(10): 19-22.)
[22] 黄晓斌. 基于协同过滤的数字图书馆推荐系统研究[J]. 大学图书馆学报, 2006, 24(1): 53-57. (Huang Xiaobin. A Study on the Digital Library Recommender System base on Collaborartive Filtering [J]. Journal of Academic Libraries, 2006, 24(1): 53-57. )
[23] 李沛东. 基于语用情境的资源推荐研究及应用[D]. 重庆: 重庆大学, 2011. (Li Peidong. Research and Application of Resource Recommendation base on Pragmatics Context [D]. Chongqing: Chongqing University, 2011.)
[24] Goldberg K, Toeder T, Gupta D, et al. Eigentaste: A Constant Time Collaborative Filtering Algorithm [J]. Information Retrieval, 2001, 4(2): 133-151.
[25] Zhou T, Kuscsik Z, Liu J G, et al. Solving the Apparent Diversity-accuracy Dilemma of Recommender Systems [J]. Proceedings of the National Academy of Sciences, 2010, 107(10): 4511-4517.
[26] Lemire D, Maclachlan A. Slope One Predictors for Online Rating Based Collaborative Filtering [C]. In: Proceedings of the 2005 SIAM International Conference on Data Mining. 2005: 471-475.
[27] Liu N N, Qiang Y. Eigenrank: A Ranking-oriented Approach to Collaborative Filtering [C]. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research & Development on Information Retrieval. 2008: 83-90.
