| dc.contributor.advisor | 陳良弼 | zh_TW |
| dc.contributor.advisor | Chen, Arbee L.P. | en_US |
| dc.contributor.author (作者) | 鄭挺拔 | zh_TW |
| dc.contributor.author (作者) | Cheng, Ting Pa | en_US |
| dc.creator (作者) | 鄭挺拔 | zh_TW |
| dc.creator (作者) | Cheng, Ting Pa | en_US |
| dc.date (日期) | 2011 | en_US |
| dc.date.accessioned | 12-四月-2012 14:12:14 (UTC+8) | - |
| dc.date.available | 12-四月-2012 14:12:14 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-四月-2012 14:12:14 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0098753015 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/52636 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 98753015 | zh_TW |
| dc.description (描述) | 100 | zh_TW |
| dc.description.abstract (摘要) | 近年來，推薦系統（recommendation system）相關研究是一個很熱門的議題，當使用者看到一篇文章，對該文章所描述的事件很感興趣，想要了解該事件的全貌，此時想要得到是該事件的通盤的見解，而非局部的意見，也就是以不同角度去解析此事件的文章清單時，若以過去傳統推薦系統的作法，推薦與這篇文章相似的文章給使用者就未必合適，因為相似文章只能反映對此事件相同角度，而非對此事件不同角度的文章。因此，本研究擬使用社群性標籤（social tag）解決以上問題。透過不同使用者標註標籤反映不同看法的機制，我們可以從文章中選出代表性的標籤，透過該標籤組與文章分數計算，找出對此事件不同角度的文章清單推薦給使用者。實驗結果顯示，若文章有較高的可信度擁有多種角度，則使用我們提出的演算法確實擁有較好的準確度。 | zh_TW |
| dc.description.tableofcontents | 第一章 導論及研究動機.............................8 第二章 相關研究..................................10 2.1使用標籤作推薦的相關研究.......................10 2.2挑選標籤的相關研究............................11 2.2.1 從多篇文章中挑選標籤 .......................11 2.2.2 從一篇文章中挑選標籤 .......................13 2.2.3 擴展相關標籤..............................14 第三章 方法描述和實作............................16 3.1文章前處理..................................16 3.2挑選標籤演算法...............................20 3.2.1 挑選標籤的想法............................20 3.2.2 挑選標籤演算法............................23 3.3文章分數計算法...............................30 第四章 實驗方法與驗證............................32 4.1實驗設計....................................33 4.1.1挑選標籤演算法速度比較實驗...................36 4.1.2推薦不同角度文章準確度比較實驗................36 4.1.3依據文章亂度推薦不同角度文章準確度比較實驗......37 4.2 實驗結果.....................................39 第五章 結論.......................................45 參考文獻.........................................46 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0098753015 | en_US |
| dc.subject (關鍵詞) | 社群性標籤 | zh_TW |
| dc.subject (關鍵詞) | 推薦系統 | zh_TW |
| dc.subject (關鍵詞) | social tag | en_US |
| dc.subject (關鍵詞) | recommendation | en_US |
| dc.title (題名) | 以社群標籤組為基礎之不同角度文章之推薦 | zh_TW |
| dc.title (題名) | Using social tags for comprehensive document recommendation | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | [1] Ciro Cattuto, Dominik Benz, Andreas Hotho and Gerd Stumme. Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems. Knowledge&Data Engineering Group, 2008. | zh_TW |
| dc.relation.reference (參考文獻) | [2] Christian Wartena, Rogier Brussee and Martin Wibbels. Using Tag Co-occurrence for Recommendation. Proceedings of the 9th ACM ISDA International Conference on Intelligent Systems Design and Applications, 2009. | zh_TW |
| dc.relation.reference (參考文獻) | [3] Petros Venetis, Georgia Koutrika and Hector Garcia-Molina. On the Selection of Tags for Tag Clouds. Proceedings of the 4th ACM WSDM international conference on Web Search and Data Mining, 2011. | zh_TW |
| dc.relation.reference (參考文獻) | [4] Dong Liu, Xian-Sheng Hua, Linjun Yang, Meng Wang and Hong-Jiang Zhang. Tag Ranking. Proceedings of the 18th ACM WWW international conference on World Wide Web, 2009. | zh_TW |
| dc.relation.reference (參考文獻) | [5] Lei Wu, Linjun Yang, Nenghai Yu and Xian-Sheng Hua. Learning to Tag. Proceedings of the 18th ACM WWW international conference on World Wide Web, 2009. | zh_TW |
| dc.relation.reference (參考文獻) | [6] Fabiano Belem, Eder Martins, Jussara Almeida, Marcos Goncalves and Gisele L. Pappa. Exploiting Co-Occurrence and Information Quality Metrics to Recommend Tags in Web 2.0 Applications. Proceedings of the 19th ACM CIKM international Conference on Information and Knowledge Management, 2010. | zh_TW |
| dc.relation.reference (參考文獻) | [7] Ning Zhang, Yuan Zhang and Jie Tang. A Tag Recommendation System for Folksonomy. Proceedings of the 2nd ACM SWSM workshop on Social Web Search and Mining, 2009. | zh_TW |
| dc.relation.reference (參考文獻) | [8] Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen and Can Wang. Personalized Tag Recommendation Using Graph-based Ranking on Multi-type Interrelated Objects. Proceedings of the 32nd international ACM SIGIR conference on Special Interest Group on Information Retrieval, 2009. | zh_TW |
| dc.relation.reference (參考文獻) | [9] Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel and Erel Uziel. Social Media Recommendation based on People and Tags. Proceedings of the 33nd international ACM SIGIR conference on Special Interest Group on Information Retrieval, 2010. | zh_TW |
| dc.relation.reference (參考文獻) | [10] Börkur Sigurbjörnsson and Roelof van Zwol. Flickr Tag Recommendation based on Collective Knowledge. Proceedings of the 17th ACM WWW international conference on World Wide Web, 2008. | zh_TW |
