English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 114993/146039 (79%)
Visitors : 54181616      Online Users : 308
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 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/111903
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111903


    Title: Landscape recommendation system using public preference mining and social influence analysis
    Authors: 郭耀煌
    Tsai, Wen-Hao
    Lin, Yan-Ting
    Lee, Kuan-Rung
    Kuo, Yau-Hwang
    Lu, Bing-Huei
    Contributors: 資訊科學系
    Keywords: Classification (of information);Collaborative filtering;Data mining;Economic and social effects;Information filtering;Intelligent control;Intelligent systems;Recommender systems;Social aspects;Collaborative filtering recommendations;Heterogeneous data sources;On-line social networks;Preference orientation;Public preferences;Social influence;Social relations;Social relationships;Social networking (online)
    Date: 2015
    Issue Date: 2017-08-10 15:16:47 (UTC+8)
    Abstract: A novel landscape recommendation system which employs public preference and social influence to classify user preference orientation is proposed in this paper. Unlike traditional content-based or collaborative filtering recommendation approaches, we collected large scale information from heterogeneous data sources to construct the public preference model for user`s feature-based preference orientation classification. Moreover, the social relation graph of target user is constructed to analyze social influence of preference between users in it. Then, the social influence of preference is calculated by social influence and interest similarity between users. The purpose of this paper is that using public preference to infer user preference and further adjusting user preference through social influence of preference from neighbors. The proposed method deals with the cold-start issue in recommendation system. There two main advantages of the proposed method are social relationship can be easily obtained from online social network and any type of recommendation system can be applied in the proposed method. In our experiment, Facebook, the most famous social media, is the platform selected for social relationship analysis. The experimental result shows our approach not only innovation but also practicable. © 2015 The authors and IOS Press. All rights reserved.
    Relation: Frontiers in Artificial Intelligence and Applications, 274, 583-592
    International Computer Symposium, ICS 2014; Taichung; Taiwan; 12 December 2014 到 14 December 2014; 代碼 111725
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.3233/978-1-61499-484-8-583
    DOI: 10.3233/978-1-61499-484-8-583
    Appears in Collections:[資訊科學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML2814View/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 Copyright Announcement
    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。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    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。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback