异质网络资源多维度推荐模式研究——以豆瓣网为例
收稿日期: 2016-11-13
修回日期: 2016-12-23
网络出版日期: 2017-02-05
基金资助
本文系国家社会科学基金重大项目"基于多维度聚合的网络资源知识发现研究"（项目编号：13&ZD183）和国家自然科学青年基金项目"基于QSIM的图书馆移动用户群体行为模拟与学习兴趣引导研究"（项目编号：71503097）研究成果之一。
Multi Dimension Recommendation Model of Heterogeneous Network Resources-A Case Study of Douban
Received date: 2016-11-13
Revised date: 2016-12-23
Online published: 2017-02-05
夏立新 , 李重阳 , 程秀峰 , 翟姗姗 . 异质网络资源多维度推荐模式研究——以豆瓣网为例[J]. 图书情报工作, 2017 , 61(3) : 6 -13 . DOI: 10.13266/j.issn.0252-3116.2017.03.001
[Purpose/significance] For heterogeneous networks containing multi-level users and multi-level resources, a variety of heterogeneous modes are summarized. On this basis, this paper proposes a multi-dimensional recommendation framework to recommend friends and resources to the target users.[Method/process] Firstly, we establish the heterogeneous relation among users and resources by grouping and analyzing all kinds of network models of them. Secondly, we recommend users of same level and secondary resources by using collaborative filtering algorithm, which is based on the secondary resource score matrix by sentiment analysis. Thirdly, we recommend primary users and resources based on heterogeneous relation.[Result/conclusion] The experiment results on douban data show that the proposed recommendation frame is suitable for the recommendation of some heterogeneous network resources.
[1] CNNIC.第37次中国互联网络发展状况统计报告[EB/OL].[2016-05-08].http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201601/t20160122_53271.htm.
[2] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[C]//COOPER G F, MORAL S. Proceedings of the 14th conference on uncertainty in artificial intelligence. San Francisco:Morgan Kaufmann Publishers Inc,2013:43-52.
[3] GUO L, MA J, CHEN Z, et al. Learning to recommend with social relation ensemble[C]//CHEN X W. Proceedings of the 21st ACM international conference on information and knowledge management.New York:ACM, 2012:2599-2602.
[4] 安维, 刘启华, 张李义. 个性化推荐系统的多样性研究进展[J]. 图书情报工作, 2013, 57(20):127-135.
[5] SHI Y Y, GE W C, WANG L Y, et al. Improving research on K-means clustering algorithms for personalized recommendation[J]. Information &communications, 2016,30(1):19-21.
[6] YANG S H, LONG B, SMOLA A, et al. Like like alike-joint friendship and interest propagation in social networks[C]//SIMPSON S. Proceedings of the 20th international conference on World Wide Web.New York:ACM, 2011:537-546.
[7] YANG A T, TANG Y, WANG J B, et al. Personalized friends recommendation system based on game theory in social network[J]. Computer science, 2015,42(9):191-194.
[8] CASTRO J, RODRIGUEZ R M, BARRANCO M J. Weighting of features in content-based filtering with entropy and dependence measures[J]. International journal of computational intelligence systems, 2014, 7(1):80-89.
[9] THOMAS R, LEO M, STEFANO DA, et al. Improving change recommendation using aggregated association rules[C]//KIM M, ROBBES R, BIRD C. Proceedings of the international conference on mining software repositories.New York:ACM, 2016:73-84.
[10] LI W, HE W. An improved collaborative filtering approach based on user ranking and item clustering[M]//PATHAN M, WEI G Y, FORTINO G. Internet and distributed computing systems. Berlin:Springer, 2013:134-144.
[11] KAI L, ZHANGK, XIANG Y C, et al. A hybrid recommendation model based on the label propagation and VSM clustering[C]//SCHERIF, GLOMEDIAZ, XGEORGIO, et al. Proceedings of international conference on computer science & education. Piscataway:IEEE,2012:911-915.
[12] 冯勇, 李军平, 徐红艳,等. 基于社会网络分析的协同推荐方法改进[J]. 计算机应用, 2013, 33(3):841-844.
[13] ZHANG J, LIN Y, LIN M, et al. An effective collaborative filtering algorithm based on user preference clustering[J]. Applied intelligence, 2016, 45(2):230-240.
[14] ZHU T, QIN C. Collaborative filtering recommendation method based on user classification[C]//TU Y L. Proceedings of the 14th Wuhan international conference on E-Business. Alfred:Alfred University,2015:67-69.
[15] GASMI I, SERIDIBOUCHELAGHEM H, HOCINE L, et al. Collaborative filtering recommendation based on dynamic changes of user interest[J]. Intelligent decision technologies, 2015, 9(3):271-281.
[16] 蒋宗礼,金益斌. 结合点评情感分析的推荐算法研究[J]. 计算机应用研究,2016,33(5):1-5.
[17] HE Y, TAN J. Study on SINA micro-blog personalized recommendation based on semanticnetwork[J]. Expert systems with applications, 2015, 42(10):4797-4804.
[18] MENDES N F M, LIMAD C A A, LOPES S E, et al. Content's personalized recommendation for implementing ubiquitous learning in health 2.0[J]. IEEE Latin America Transactions, 2014, 12(8):1515-1522.
[19] HU X, WANG W, GONG X, et al. Friends recommendation with rating side information[J]. Open cybernetics & systemics journal, 2015, 9(1):807-815.
[20] XU B, YANG D. Study partners recommendation for xMOOCs learners[J]. Computational intelligence &neuroscience, 2015(1):1-10.
[21] ADOMAVICIUS G, SANKARANARAYANAN R, Sen S, et al. Incorporating contextual information in recommender systems using a multidimensional approach[J]. ACM transactions on information systems, 2005, 23(1):103-145.
[22] 吴冉, 黄超, 卢盛祺. 基于多维度推荐的智慧在线学习系统[J]. 计算机系统应用, 2015, 24(12):88-92.
[23] 邢哲, 梁竞帆, 朱青. 多维度自适应的协同过滤推荐算法[J]. 小型微型计算机系统, 2011, 32(11):2210-2216.
[24] 李亚静. 基于SNA的内容型社交网络结构研究[D]. 天津:天津师范大学, 2012.
[25] 李长玲, 魏绪秋, 冯志刚,等. 基于2-模异质网络的作者潜在合作空间测度与识别-以图书情报学为例[J]. 图书情报工作, 2015,59(12):93-99.
[26] 张伟哲, 王佰玲, 何慧,等. 基于异质网络的意见领袖社区发现[J]. 电子学报, 2012, 40(10):1927-1932.
[27] GAN M, JIANG R. ROUND:walking on an object-user heterogeneous network for personalized recommendations[J]. Expert systems with applications, 2015, 42(22):8791-8804.
[28] 盈艳, 曹妍, 牟向伟. 基于项目评分预测的混合式协同过滤推荐[J]. 现代图书情报技术, 2015(6):27-32.
[29] 张霁. 论豆瓣网的小众聚合功能[D]. 成都:四川省社会科学院, 2012.
[30] MISLOVE A, MARCON M, GUMMADI K P, et al. Measurement and analysis of online social networks[C]//MISLOVE A. Proceedings of the 7th ACM SIGCOMM conference on internet measurement. New York:ACM, 2007:1815-1816.
[31] 吴昊, 刘东苏. 社交网络中的好友推荐方法研究[J]. 现代图书情报技术, 2015(1):59-65.
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