收稿日期: 2015-06-09
修回日期: 2015-09-07
网络出版日期: 2015-11-20
A New Method of Social Information Recommendation by Combining Contextual Factors
Received date: 2015-06-09
Revised date: 2015-09-07
Online published: 2015-11-20
[目的/意义] 针对目前融合情境因素的信息推荐方法大都存在推荐前的情境过滤(pre-filtering)和推荐后的情境过滤(post-filtering)所导致的价值信息流失问题,将情境因素融入到推荐过程中,实现基于用户-资源-情境的多维推荐。[方法/过程] 将情境因素融入推荐的过程中,动态挖掘在不同情境下用户兴趣的偏好,利用社会网络的相关指标赋予用户兴趣初始值,从空间距离的视角计算用户兴趣的权重,最后,借鉴内容过滤和协同推荐的思想实现用户的评分预测,进而按照用户的兴趣进行推荐。[结果/结论] 与以往二维推荐的实验比较表明,将情境因素融入到推荐过程中的方法在减少价值流失的基础上,能更为准确地揭示用户的兴趣,提高推荐质量,为存在社会关系的社会化媒体推荐服务提供借鉴。
房小可 . 一种融合情境因素的社会化信息推荐新方法[J]. 图书情报工作, 2015 , 59(22) : 105 -111,129 . DOI: 10.13266/j.issn.0252-3116.2015.22.016
[Purpose/significance] Since the current information recommendation method integrating contextual factors leads to the loss of information valuedue to excessive filtration. This papercompletes multidimensional recommendation based on use-resource-context.[Method/process] For such problems, this paper integrated context factors into the recommending process, and completed users'interest miningunder different contexts. Firstly, an initial interest value was given using the index of socialnetworks. Then the weight for users' interest was proposed from the perspective ofspatial distance. Finally, users' interest score prediction was implemented according tocollaborative recommendation, and the interest recommendations for the users was completed. [Result/conclusion] The comparison between the two-dimensional recommendation and the multidimensional recommendation shows that the recommendation integrating context factors can more accurately reveal the users' interest and improve the quality of recommendation, and provide a reference for social media recommendation service.
[1] 中国互联网络信息中心(CNNIC). 第35次中国互联网络发展状况统计报告[EB/OL].[2015-02-02]. http://www.cnii.com.cn/ internetnews/img/site2/20150204/00016c58d8d5163bb 74e2f.pdf.
[2] Li Yung-Ming, Hsiao Han-Wen, Li Yi-Lin. Recommending social network applications via social filtering mechanisms[J]. Information Sciences,2013,2,39(1):18-30.
[3] Xing Xing, Zhang Weishi, Zhi Chun. Learning to recommend top-k items in online social networks[C]//World Congress on Information and Communication Technology. Trivandrum:IEEE,2012:1171-1176.
[4] Rawashdeh M, Kim H N, Alja'am J M, et al. Folksonomy link prediction based on a tripartite graph for tag recommendation[J]. Journal of Intelligent Information System,2013,40(2):307-325.
[5] Kim H, Kim H J. A framework for tag-aware recommender systems[J]. Expert System with Applications,2014,41(8):4000-4009.
[6] Li Wenlong, He Wei. An improved collaborative filtering approach based on user ranking and item clustering[C]// Proceedings of 6th International Conference on Internet and Distributed Computing Systems.Heidelberg:Spring, 2013:28-30.
[7] Castor 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.
[8] Lei Kai, Zhang Kun, Xiang Yanchao, et al. A hybrid recommendation model based on the label propagation and VSM clustering[C]//Proceedings of 2012 7th International Conference on Computer Science and Education.Melbourne, VIC: IEEE, 2012:911-915.
[9] Dey A K. Providing architectural support for building context aware applications[D]. Atlanta: Georgia Institute of Technology,2000.
[10] Lee W P, Lee K H. Making smartphone service recommendations by predicting users' intentions: A context-aware approach[J]. Information Sciences,2014,277(1):21-35.
[11] Palmisano C, Tuzhilin A, Gorgoglione M. Using context to improve predictive modeling of customers in personalization applications[J]. IEEE Transactions on Knowledge and DATA Engineering,2008,20(11):1535-1549.
[12] Prahalad C K. Beyond CRM: Predicts customer-context is the next big thing[M]. New York: AMACCM Press, 2004.
[13] Adomavicius G, Tuzhilin A. Context-aware recommender system[M]//Recommender System Handbook.New York:Springor, 2010:217-253.
[14] Toon D P, Tom D, Luc M. Context aware recommendations for user-generated content on a social network site[C]//Proceedings of the Seventh European Conference on European Interactive Television Conference.Now York:ACM,2009:133-136.
[15] Oku K, Nakajima S, Miyazaki J, et al. Context-aware SVM for context-dependent information recommendation [C]//Proceedings of the 7th International Conference on Mobile Data Management.Washington DC:IEEE,2006.
[16] Jung J J. Contextualized mobile recommendation service based on interactive social network discovered from mobile users[J]. Expert Systems with Applications, 2009, 36(9): 11950-11956.
[17] Jorge G A, Paloma D J, Elena G H. A hybrid collaborative filtering system for contextual recommendations in social networks[J].Discovery Science,2009(5808):393-400.
[18] Kim S, Kwon J. Effective context-aware recommendation on the semantic Web[J]. IJCSNS International Journal of Computer Science and Network Security, 2007,7(8):154-159.
[19] Karatzoglou A, Amatriain X, Baltrunas L, et al. Multiverse recommendation:N-dimensional tensor factorization for context-aware collaborative filtering[C]// Proceedings of the Fourth ACM Conference on Recommender System.New York: ACM, 2010:79-86.
[20] 刘非凡,李长玲,魏绪秋. 基于2-模网络和G-N社群类算法的潜在合作者研究——以国内图情领域的社会网络分析研究为例[J]. 情报理论与实践,2014,37(6):117-122.
[21] Pazzani M. A framework for collaborative, content-based and demographic filtering[J]. Aritificial Intelligence Review,1999,13(5/6):393-408.
[22] 董旭,魏振军. 一种加权欧氏距离聚类方法[J]. 信息工程大学学报,2005,6(1):23-25.
[23] 《知网》情感分析用词语集[EB/OL].[2015-06-18].http://www.keenage.com/html/c_bulletin_2007.htm.
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