English  |  正體中文  |  简体中文  |  Items with full text/Total items : 88295/117812 (75%)
Visitors : 23405671      Online Users : 171
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/78905
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/78905


    Title: Labeled influence maximization in social networks for target marketing
    Authors: Li, Fa Hsien;Li, Cheng Te;Shan, Man-Kwan
    李法賢;林守德;沈錳坤
    Contributors: 資科系
    Keywords: Greedy method;Influence maximizations;Maximum coverage;Maximum spread;Novel algorithm;Offline;Product information;Proximity;Social networks;Target marketing;Viral marketing;Algorithms;Customer satisfaction;Profitability;Sales;Seed;Social networking (online);Social sciences computing;Economic and social effects
    Date: 2011
    Issue Date: 2015-10-08 17:48:57 (UTC+8)
    Abstract: The influence maximization problem is to find a set of seed nodes which maximize the spread of influence in a social network. The seed nodes are used for the viral marketing to gain the maximum profits through the effective word-of-mouth. However, in more real-world cases, marketers usually target certain products at particular groups of customers. While original influence maximization problem considers no product information and target customers, in this paper, we focus on the target marketing. We propose the labeled influence maximization problem, which aims to find a set of seed nodes which can trigger the maximum spread of influence on the target customers in a labeled social network. We propose three algorithms to solve such labeled influence maximization problem. We first develop the algorithms based on the greedy methods of original influence maximization by considering the target customers. Moreover, we develop a novel algorithm, Maximum Coverage, whose central idea is to offline compute the pairwise proximities of nodes in the labeled social network and online find the set of seed nodes. This allows the marketers to plan and evaluate strategies online for advertised products. The experimental results on IMDb labeled social network show our methods can achieve promising performances on both effectiveness and efficiency. © 2011 IEEE.
    Relation: Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1109/PASSAT/SocialCom.2011.152
    DOI: 10.1109/PASSAT/SocialCom.2011.152
    Appears in Collections:[資訊科學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML678View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback