微信服务号

微信订阅号

2025年4月7日 0:15 星期一
首页 > 过刊浏览>2021年第32卷第2期 >424-444. DOI:10.13328/j.cnki.jos.006145
PDF HTML阅读 XML下载 导出引用 引用提醒
基于事件社会网络推荐系统综述
作者:
作者简介:

廖国琼(1969-),男,博士,教授,博士生导师,CCF高级会员,主要研究领域为大数据技术,数据库,数据挖掘,社会网络,推荐系统,物联网.
蓝天明(1978-),男,博士生,主要研究领域为数据挖掘,社会网络,推荐系统.
黄晓梅(1977-),女,博士生,主要研究领域为数据挖掘,社会网络,推荐系统.
陈辉(1976-),男,博士,教授,博士生导师,CCF高级会员,主要研究领域为数据库与Web挖掘技术.
万常选(1962-),男,博士,教授,博士生导师,CCF杰出会员,主要研究领域为Web数据管理,XML信息检索,金融数据挖掘,情感计算.
刘德喜(1975-),男,博士,教授,博士生导师,CCF高级会员,主要研究领域为Web数据管理,XML信息检索,自然语言处理.
刘喜平(1981-),男,博士,教授,博士生导师,CCF专业会员,主要研究领域为大数据分析,面向金融领域的人工智能技术,数据库技术.

通讯作者:

蓝天明,E-mail:67622035@qq.com

基金项目:

国家自然科学基金（61772245，61962024）


Survey on Recommendation Systems in Event-based Social Networks
Author:
Fund Project:

National Natural Science Foundation of China (61772245, 61962024)

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [102]
    • |
  • 相似文献 [20]
    • | | |
  • 文章评论
    摘要:

    基于事件社会网络（event-based social network，简称EBSN）是一种结合了线上网络和线下网络的新型社会网络，近年来得到了越来越多的关注，已有许多国内外重要研究机构的研究者对其进行研究并取得了许多研究成果.在EBSN推荐系统中，一个重要的任务就是设计出更好、更合理的推荐算法以提高推荐精确度和用户满意度，其关键在于充分结合EBSN中的各种上下文信息去挖掘用户、事件和群组的隐藏特征.主要对EBSN推荐系统的最新研究进展进行综述.首先，概述EBSN的定义、结构、属性和特征，介绍EBSN推荐系统的基本框架，并分析EBSN推荐系统与其他推荐系统的区别；其次，对EBSN推荐系统的主要推荐方法和推荐内容进行归纳、总结和对比分析；最后，分析EBSN推荐系统的研究难点及其发展趋势，并给出总结.

    Abstract:

    Event-based social network (EBSN) is a new type of social network combining online network and offline network, which has received more and more attentions in recent years. There have been many researchers in important research institutions domestic and abroad to study it and they have achieved a lot of research results. In an EBSN recommendation system, one important task is to design better and more reasonable recommendation algorithms to improve recommendation accuracy and user satisfaction. The key is to fully combine various contextual information in EBSN to mine the hidden features of users, events, and groups. This study mainly reviews the latest research progress of the EBSN recommendation system. First, the definition, structure, attributes, and characteristics of EBSN are outlined, the basic framework of EBSN recommendation systems is introduced, and the differences between EBSN recommendation system and other recommendation systems are analyzed. Secondly, the main recommendation methods and recommended contents of the EBSN recommendation system are generalized, summarized, compared, and analyzed. Finally, the research difficulties and development future trends of the EBSN recommendation system are analyzed, and conclusions of the study are drawn.

    参考文献
    [1] Liu X, He Q, Tian Y, et al. Event-based social networks:Linking the online and offline social worlds. In:Proc. of the 18th ACM Int'l Conf. on Knowledge Discovery and Data Mining (KDD). 2012. 1032-1040.
    [2] Macedo AQD, Marinho LB. Event recommendation in event-based social networks. In:Proc. of the 28th AAAI Conf. on Artificial Intelligence (AAAI). 2014. 3130-3131.
    [3] Jannach D, Zanker M, Felfernig A, et al. Recommender Systems:An Introduction. New York:Cambridge University Press, 2010.
    [4] Guy I, Carmel D. Social recommender systems. In:Proc. of the 20th Int'l Conf. Companion on World Wide Web (WWW). 2011. 283-284.
    [5] Yang X, Guo Y, Liu Y, et al. A survey of collaborative filtering based social recommender systems. Computer Communications, 2013,41(5):1-10.
    [6] Gao H, Liu H. Data analysis on location-based social networks. In:Proc. of the Mobile Social Networking. New York:Springer-Verlag, 2014. 165-194.
    [7] Liu SD, Meng XW. Recommender system in location-based social networks. Chinese Journal of Computers, 2015,38(2):322-336(in Chinese with English abstract).
    [8] Yin H, Zou L, Nguyen QVH, et al. Joint event-partner recommendation in event-based social networks. In:Proc. of the 34th IEEE Int'l Conf. on Data Engineering (ICDE). 2018. 929-940.
    [9] Zhang Y, Wu H, Panangadan A, et al. Integration of heterogeneous Web services for event-based social networks. In:Proc. of the 16th IEEE Int'l Conf. on Information Reuse and Integration (IRI). 2015. 57-63.
    [10] Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. Journal of Machine Learning Research, 2003,3:993-1022.
    [11] Wang Y, Tang J. Event2Vec:Learning event representations using spatial-temporal information for recommendation. In:Proc. of the 23rd Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD). 2019. 314-326.
    [12] Rendle S, Freudenthaler C, Gantner Z, et al. BPR:Bayesian personalized ranking from implicit feedback. In:Proc. of the 25th Conf. on Uncertainty in Artificial Intelligence (UAI). 2009. 452-461.
    [13] Macedo AQ, Marinho LB, Santos RLT. Context-aware event recommendation in event-based social networks. In:Proc. of the 9th ACM Conf. on Recommender Systems (RecSys). 2015. 123-130.
    [14] Cao J, Zhu Z, Shi L, et al. Multi-feature based event recommendation in event-based social network. Int'l Journal of Computational Intelligence Systems, 2018,11(1):618-633.
    [15] Li S, Cheng X, Su S, et al. Exploiting organizer influence and geographical preference for new event recommendation. Expert Systems, 2017,34(2):1-14.
    [16] Ogundele TJ, Chow CY, Zhang JD. Eventrec:Personalized event recommendations for smart event-based social networks. In:Proc. of the 3rd IEEE Int'l Conf. on Smart Computing (SMARTCOMP). 2017. 1-8.
    [17] Wang Z, He P, Shou L, et al. Toward the new item problem:Context-enhanced event recommendation in event-based social networks. In:Proc. of the 37th European Conf. on Information Retrieval (ECIR). 2015. 333-338.
    [18] Wang Z, Zhang Y, Li Y, et al. Exploiting social influence for context-aware event recommendation in event-based social networks. In:Proc. of the 36th IEEE Conf. on Computer Communications (INFOCOM). 2017. 1-9.
    [19] Yu Z, Du R, Guo B, et al. Who should I invite for my party? Combining user preference and influence maximization for social events. In:Proc. of the 17th ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing (UbiComp). 2015. 879-883.
    [20] Zhang Y, Wu H, Sorathia VS, et al. Event recommendation in social networks with linked data enablement. In:Proc. of the 15th Int'l Conf. on Enterprise Information Systems (ICEIS), Vol.2. 2013. 371-379.
    [21] Du Y, Meng X, Zhang Y, et al. GERF:A group event recommendation framework based on learning-to-rank. IEEE Trans. on Knowledge and Data Engineering, 2019,32(4):674-687.
    [22] De Lira VM, Macdonald C, Ounis I, et al. Exploring social media for event attendance. In:Proc. of the 9th IEEE/ACM Int'l Conf. on Advances in Social Networks Analysis and Mining (ASONAM). 2017. 447-450.
    [23] Zhang X, Zhao J, Cao G. Who will attend?-Predicting event attendance in event-based social network. In:Proc. of the 16th IEEE Int'l Conf. on Mobile Data Management (MDM). 2015. 74-83.
    [24] Xu M, Liu S. Semantic-enhanced and context-aware hybrid collaborative filtering for event recommendation in event-based social networks. IEEE Access, 2019,7:17493-17502.
    [25] Trinh T, Wu D, Wang R, et al. An effective content-based event recommendation model. In:Proc. of the Multimedia Tools and Applications. 2020. 1-20.
    [26] Ding H, Yu C, Li G, et al. Event participation recommendation in event-based social networks. In:Proc. of the 7th Int'l Conf. on Social Informatics (SocialInformatics). 2016. 361-375.
    [27] Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems. Computer, 2009,42(8):30-37.
    [28] Salakhutdinov R, Mnih A. Probabilistic matrix factorization. In:Proc. of the 20th Int'l Conf. on Neural Information Processing Systems (NIPS). 2007. 1257-1264.
    [29] Singh AP, Gordon GJ. Relational learning via collective matrix factorization. In:Proc. of the 14th ACM Int'l Conf. on Knowledge Discovery and Data Mining (KDD). 2008. 650-658.
    [30] Koren Y. Factorization meets the neighborhood:A multifaceted collaborative filtering model. In:Proc. of the 14th ACM Int'l Conf. on Knowledge Discovery and Data Mining (KDD). 2008. 426-434.
    [31] Cichocki A, Zdunek R, Amari S. Nonnegative matrix and tensor factorization. IEEE Signal Processing Magazine, 2007,25(1):142-145.
    [32] Zhang W, Wang J. A collective bayesian poisson factorization model for cold-start local event recommendation. In:Proc. of the 21th ACM Int'l Conf. on Knowledge Discovery and Data Mining (KDD). 2015. 1455-1464.
    [33] Qiao Z, Zhang P, Cao Y, et al. Combining heterogenous social and geographical information for event recommendation. In:Proc. of the 28th AAAI Conf. on Artificial Intelligence (AAAI). 2014. 145-151.
    [34] Du R, Yu Z, Mei T, et al. Predicting activity attendance in event-based social networks:Content, context and social influence. In:Proc. of the 16th ACM Int'l Joint Conf. on Pervasive and Ubiquitous Computing. 2014. 425-434.
    [35] Li M, Huang D, Wei B, et al. Event recommendation via collective matrix factorization with event-user neighborhood. In:Proc. of the 5th Int'l Conf. on Intelligent Science and Big Data Engineering. 2017. 676-686.
    [36] Liao Y, Lam W, Bing L, et al. Joint modeling of participant influence and latent topics for recommendation in event-based social networks. ACM Trans. on Information Systems, 2018,36(3):1-31.
    [37] Jhamb Y, Fang Y. A dual-perspective latent factor model for group-aware social event recommendation. Information Processing and Management, 2017,53(3):559-576.
    [38] Ji X, Qiao Z, Xu M, et al. Online event recommendation for event-based social networks. In:Proc. of the 24th Int'l Conf. on World Wide Web (WWW). 2015. 45-46.
    [39] Gao L, Wu J, Qiao Z, et al. Collaborative social group influence for event recommendation. In:Proc. of the 25th ACM Int'l on Conf. on Information and Knowledge Management (CIKM). 2016. 1941-1944.
    [40] Gu Y, Song J, Liu W, et al. Context aware matrix factorization for event recommendation in event-based social networks. In:Proc. of the 15th IEEE/WIC/ACM Int'l Conf. on Web Intelligence (WI). 2016. 248-255.
    [41] Liu CY, Zhou C, Wu J, et al. CPMF:A collective pairwise matrix factorization model for upcoming event recommendation. In:Proc. of the 14th IEEE Int'l Joint Conf. on Neural Networks (IJCNN). 2017. 1532-1539.
    [42] Bok K, Lee S, Choi D, et al. Recommending personalized events based on user preference analysis in event based social networks. In:Proc. of the Electronic Commerce Research. 2019. 1-19.
    [43] Lu Y, Qiao Z, Zhou C, et al. Location-aware friend recommendation in event-based social networks:A bayesian latent factor approach. In:Proc. of the 25th ACM Int'l on Conf. on Information and Knowledge Management (CIKM). 2016. 1957-1960.
    [44] Li S, Cheng X, Su S, et al. Followee recommendation in event-based social networks. In:Proc. of the 21st Int'l Conf. on Database Systems for Advanced Applications (DASFAA). 2016. 27-42.
    [45] Jiang JY, Li CT. Who should I invite:Predicting event participants for a host user. Knowledge and Information Systems, 2019, 59(3):629-650.
    [46] Wang Z, Zhang Y, Chen H, et al. Deep user modeling for content-based event recommendation in event-based social networks. In:Proc. of the 37th IEEE Conf. on Computer Communications (INFOCOM). 2018. 1304-1312.
    [47] Liao Y, Lin X, Lam W. Whether this participant will attract you to this event? Exploiting participant influence for event recommendation. In:Proc. of the 16th IEEE Int'l Conf. on Data Mining (ICDM). 2016. 1035-1040.
    [48] Meng XW, Liu SD, Zhang YJ, Hu X. Research on social recommender systems. Ruan Jian Xue Bao/Journal of Software, 2015, 26(6):1356-1372(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4831.htm[doi:10.13328/j.cnki.jos.004831]
    [49] Spitzer F. Principles of Random Walk. 2nd ed, Berlin:Springer Science and Business Media, 2013.
    [50] Tong H, Faloutsos C, Pan JY. Fast random walk with restart and its applications. In:Proc. of the IEEE 6th Int'l Conf. on Data Mining (ICDM). 2006. 613-622.
    [51] Haveliwala TH. Topic-sensitive pagerank:A context-sensitive ranking algorithm for Web search. IEEE Trans. on Knowledge and Data Engineering, 2003,15(4):784-796.
    [52] Liu S, Wang B, Xu M. Event recommendation based on graph random walking and history preference reranking. In:Proc. of the 40th ACM Int'l Conf. on Research and Development in Information Retrieval (SIGIR). 2017. 861-864.
    [53] Pham TN, Li X, Cong G, et al. A general graph-based model for recommendation in event-based social networks. In:Proc. of the 31st IEEE Int'l Conf. on Data Engineering (ICDE). 2015. 567-578.
    [54] Li B, Wang B, Mo Y, et al. A novel random walk and scale control method for event recommendation. In:Proc. of the 1st IEEE Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld). 2016. 228-235.
    [55] Liu S, Wang B, Xu M. Serge:Successive event recommendation based on graph entropy for event-based social networks. IEEE Access, 2017,6:3020-3030.
    [56] Liu S, Wang B, Xu M, et al. Evolving graph construction for successive recommendation in event-based social networks. Future Generation Computer Systems, 2019,96:502-514.
    [57] Zhang S, Lv Q. Hybrid EGU-based group event participation prediction in event-based social networks. Knowledge-based Systems, 2018,143:19-29.
    [58] Liao G, Huang X, Mao M, et al. Group event recommendation in event-based social networks considering unexperienced events. IEEE Access, 2019,7:96650-96671.
    [59] Lu M, Dai Y, Zhong H, et al. MFDAU:A multi-features event participation prediction model for users of different activity levels. Physica A:Statistical Mechanics and its Applications, 2019,534:Article No.122244
    [60] Boutsis I, Karanikolaou S, Kalogeraki V. Personalized event recommendations using social networks. In:Proc. of the 16th IEEE Int'l Conf. on Mobile Data Management (MDM). 2015. 84-93.
    [61] Hofmann T, Puzicha J. Latent class models for collaborative filtering. In:Proc. of the 16th Int'l Joint Conf. on Artificial Intelligence (IJCAI). 1999. 688-693.
    [62] Yin H, Hu Z, Zhou X, et al. Discovering interpretable geo-social communities for user behavior prediction. In:Proc. of the 32nd IEEE Int'l Conf. on Data Engineering (ICDE). 2016. 942-953.
    [63] Yuan Q, Cong G, Lin CY. COM:A generative model for group recommendation. In:Proc. of the 20th ACM Int'l Conf. on Knowledge Discovery and Data Mining (KDD). 2014. 163-172.
    [64] Purushotham S, Kuo CCJ. Modeling group dynamics for personalized group-event recommendation. In:Proc. of the 9th Int'l Conf. on Social Computing, Behavioral-cultural Modeling, and Prediction. 2015. 405-411.
    [65] Ji K, Chen Z, Sun R, et al. GIST:A generative model with individual and subgroup-based topics for group recommendation. Expert Systems with Applications, 2018,94:81-93.
    [66] Yin H, Sun Y, Cui B, et al. LCARS:A location-content-aware recommender system. In:Proc. of the 19th ACM Int'l Conf. on Knowledge Discovery and Data Mining (KDD). 2013. 221-229.
    [67] Yin H, Cui B, Chen L, et al. Modeling location-based user rating profiles for personalized recommendation. ACM Trans. on Knowledge Discovery from Data, 2015,9(3):1-41.
    [68] Jeong HJ, Kim MH. HGGC:A hybrid group recommendation model considering group cohesion. Expert Systems with Applications, 2019,136:73-82.
    [69] Du Y, Meng X, Zhang Y. CVTM:A content-venue-aware topic model for group event recommendation. IEEE Trans. on Knowledge and Data Engineering, 2019,32(7):1290-1303.
    [70] Purushotham S, Kuo CCJ. Personalized group recommender systems for location-and event-based social networks. ACM Trans. on Spatial Algorithms and Systems, 2016,2(4):1-29.
    [71] Zhang S, Yao L, Sun A, et al. Deep learning based recommender system:A survey and new perspectives. ACM Computing Surveys, 2019,52(1):1-38.
    [72] Wu X, Dong Y, Shi B, et al. Who will attend this event together? Event attendance prediction via deep LSTM networks. In:Proc. of the 18th SIAM Int'l Conf. on Data Mining (ICDM). 2018. 180-188.
    [73] Jhamb Y, Ebesu T, Fang Y. Attentive contextual denoising autoencoder for recommendation. In:Proc. of the 41st ACM SIGIR Int'l Conf. on Theory of Information Retrieval (SIGIR). 2018. 27-34.
    [74] Luceri L, Braun T, Giordano S. Social influence (deep) learning for human behavior prediction. In:Proc. of the 7th Int'l Workshop on Complex Networks. 2018. 261-269.
    [75] Pramanik S, Haldar R, Kumar A, et al. Deep learning driven venue recommender for event-based social networks. IEEE Trans. on Knowledge and Data Engineering, 2020,32(11):2129-2143.
    [76] Vinh Tran L, Nguyen Pham TA, Tay Y, et al. Interact and decide:Medley of sub-attention networks for effective group recommendation. In:Proc. of the 42nd ACM SIGIR Int'l Conf. on Research and Development in Information Retrieval (SIGIR). 2019. 255-264.
    [77] Li B, Wang G, Cheng Y, et al. An event recommendation model using ELM in event-based social network. In:Proc. of the Neural Computing and Applications. 2019. 1-10.
    [78] Tong Y, Meng R, She J. On bottleneck-aware arrangement for event-based social networks. In:Proc. of the 31st IEEE Int'l Conf. on Data Engineering Workshops. 2015. 216-223.
    [79] She J, Tong Y, Lei C. Utility-aware social event-participant planning. In:Proc. of the 15th ACM Int'l Conf. on Management of Data (SIGMOD). 2015. 1629-1643.
    [80] She J, Tong Y, Chen L, et al. Conflict-aware event-participant arrangement. In:Proc. of the 31st IEEE Int'l Conf. on Data Engineering (ICDE). 2015. 735-746.
    [81] She J, Tong Y, Chen L, et al. Feedback-aware social event-participant arrangement. In:Proc. of the 43rd ACM Int'l Conf. on Management of Data (SIGMOD). 2017. 851-865.
    [82] Liang Y, Zhu H, Chen X. Result diversification in event-based social networks. In:Proc. of the 17th Int'l Conf. on Web-Age Information Management (WAIM). 2016. 198-210.
    [83] Liang Y. Complex dynamic event participant in an event-based social network:A three-dimensional matching. IEEE Access, 2019, 7:144188-144201.
    [84] Kou F, Zhou Z, Cheng H, et al. Interaction-aware arrangement for event-based social networks. In:Proc. of the 35th IEEE Int'l Conf. on Data Engineering (ICDE). 2019. 1638-1641.
    [85] Cheng Y, Yuan Y, Chen L, et al. Complex event-participant planning and its incremental variant. In:Proc. of the 33rd IEEE Int'l Conf. on Data Engineering (ICDE). 2017. 859-870.
    [86] Xin J, Li M, Xu W, et al. Efficient complex social event-participant planning based on heuristic dynamic programming. In:Proc. of the 23rd Int'l Conf. on Database Systems for Advanced Applications (DASFAA). 2018. 264-279.
    [87] Bikakis N, Kalogeraki V, Gunopulos D. Social event scheduling. In:Proc. of the 34th IEEE Int'l Conf. on Data Engineering (ICDE). 2018. 1272-1275.
    [88] Ding L, Zhang H, Chen Z, et al. Utility-time social event planning on EBSN. In:Proc. of the 20th IEEE Int'l Conf. on Mobile Data Management (MDM). 2019. 182-190.
    [89] Li B, Cheng Y, Wang G, et al. Incremental bilateral preference stable planning over event based social networks. In:Proc. of the Complexity. 2019. 1-12.
    [90] Burke R. Hybrid recommender systems:Survey and experiments. User Modeling and User-adapted Interaction, 2002,12(4):331-370.
    [91] Cheng Y, Yuan Y, Chen L, et al. Event-participant and incremental planning over event-based social networks. IEEE Trans. on Knowledge and Data Engineering, 2021,33(2):474-488.
    [92] Trinh T, Nguyen NT, Wu D, et al. A new location-based topic model for event attendees recommendation. In:Proc. of the 17th IEEE Int'l Conf. on Computing and Communication Technologies (RIVF). 2019. 1-6.
    [93] Wu D, Zhu Y, Jensen CS. In good company:Efficient retrieval of the top-k most relevant event-partner pairs. In:Proc. of the 24th Int'l Conf. on Database Systems for Advanced Applications (DASFAA). 2019. 519-535.
    [94] Chung T, Han J, Choi D, et al. Privacy leakage in event-based social networks:A meetup case study. Proc. of the ACM on Human- Computer Interaction, 2017,1:1-22.
    [95] Dong C, Zhou B. Privacy inference analysis on event-based social networks. In:Proc. of the 7th Int'l Conf. on Social Informatics. 2016. 421-438.
    [96] Herlocker JL, Konstan JA, Terveen LG, et al. Evaluating collaborative filtering recommender systems. ACM Trans. on Information Systems, 2004,22(1):5-53.
    [97] Gunawardana A, Shani G. Evaluating recommender systems. Recommender Systems Handbook. 2nd ed., Berlin:Springer-Verlag, 2015. 265-308.
    [98] Meng XW, Liang B, Du YL, Zhang YJ. A survey of evaluation for location-based mobile recommender systems. Chinese Journal of Computers, 2019,42(12):2695-2721(in Chinese with English abstract).
    附中文参考文献:
    [7] 刘树栋,孟祥武.基于位置的社会化网络推荐系统.计算机学报,2015,38(2):322-336.
    [48] 孟祥武,刘树栋,张玉洁,胡勋.社会化推荐系统研究.软件学报,2015,26(6):1356-1372. http://www.jos.org.cn/1000-9825/4831.htm[doi:10.13328/j.cnki.jos.004831]
    [98] 孟祥武,梁弼,杜雨露,张玉洁.基于位置的移动推荐系统效用评价研究.计算机学报,2019,42(12):2695-2721.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

廖国琼,蓝天明,黄晓梅,陈辉,万常选,刘德喜,刘喜平.基于事件社会网络推荐系统综述.软件学报,2021,32(2):424-444

复制
分享
文章指标
  • 点击次数:4274
  • 下载次数: 7863
  • HTML阅读次数: 3470
  • 引用次数: 0
历史
  • 收稿日期:2019-12-11
  • 最后修改日期:2020-04-29
  • 在线发布日期: 2020-10-12
  • 出版日期: 2021-02-06
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第19795250位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号