| dc.contributor.advisor | 陳良弼 | zh_TW |
| dc.contributor.advisor | Chen, Arbee L.P. | en_US |
| dc.contributor.author (Authors) | 張筑鈞 | zh_TW |
| dc.contributor.author (Authors) | Chang, Chu Chun | en_US |
| dc.creator (作者) | 張筑鈞 | zh_TW |
| dc.creator (作者) | Chang, Chu Chun | en_US |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 9-May-2016 15:29:19 (UTC+8) | - |
| dc.date.available | 9-May-2016 15:29:19 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-May-2016 15:29:19 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0967530361 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/95272 | - |
| 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) | 96753036 | zh_TW |
| dc.description.abstract (摘要) | 在WEB2.0的時代，網際網路中充斥著各式各樣的互動式平台。就音樂網站而言，使用者除了聽音樂外，更開始習慣於虛擬空間中交流及分享意見，並且在這些交流、分享的過程中留下他們的足跡，間接的提供許多帶有個人色彩的資訊。利用這些資訊，更貼近使用者的推薦系統因應而生。本研究中，將針對使用者過去存取過的音樂特徵以及使用者於系統中留下的文字評論特徵這兩個部份的資料，做音樂特徵的擷取、找尋具有價值的音樂特徵區間、建立使用者音樂特徵偏好，以及文字特徵的擷取、建立使用者文字特徵偏好。接著，採用協同式推薦方式，將具有相同興趣的使用者分於同一群，推薦給使用者與之同群的使用者的喜好物件，但這些推薦之物件為該使用者過去並沒有任何記錄於這些喜好物件上之物件。我們希望對於音樂推薦考慮的開始不只是音樂上之特徵，更包含了使用者交流、互動中留下的訊息。 | zh_TW |
| dc.description.abstract (摘要) | In the era of Web2.0, it is flooded with a variety of interactive platforms on the internet. In terms of music web site, in addition to listening to music, users got used to exchanging their comments and sharing their experiences through virtual platforms. And through the process of exchanging and sharing, they left their footprints. These footprints indirectly provide more information about users that contains personal characteristics. Moreover, from this information, we can construct a music recommendation system, which provides personalized service. In this research, we will focus on user’s access histories and comments of users to recommend music. Moreover, the user’s access histories are analyzed to derive the music features, then to find the valuable range of music features, and construct music profiles of user interests. On the other hand, the comments of users are analyzed to derive the textual features, then to calculate the importance of textual features, and finally to construct textual profiles of user interests. The music profile and the textual profile are behaviors for user grouping. The collaborative recommendation methods are proposed based on the favorite degrees of the users to the user groups they belong to. | en_US |
| dc.description.abstract (摘要) | 第一章、序論................................. 1 第二章、相關研究................................. 3 2.1 推薦系統................................. 3 2.2 推薦方法................................. 4 2.3 音樂推薦系統................................. 5 2.4 音樂特徵擷取................................. 6 2.5 文字特徵擷取................................. 6 第三章、研究方法................................. 8 3.1 使用者特徵擷取（User Profile Extractor）................................. 10 3.1.1 音樂偏好管理（Music Profile Manager）................................. 10 3.1.2 文字偏好管理（Textual Profile Manager）................................. 19 3.1.3 相似度距離定義（Definition of Similarity Distance）................................. 21 3.2 推薦模組（Recommendation Module）................................. 21 3.2.1 特徵結合（Feature Combination）................................. 22 3.2.2 協同式推薦（Collaborative Recommendation）................................. 25 第四章、驗證方法................................. 26 4.1 使用者音樂偏好檔案之效能（Effectiveness of the Music Profile）......................... 26 4.2 評估使用者標籤以及使用者評論之品質（Evaluation the quality of User Tags and User Comment）................................. 28 4.3 推薦結果評估方法（The evaluation method of Recommendations）......................... 30 4.4 特徵結合方法之比較（Compare with Combination methods）................................. 31 第五章、結論................................. 35 第六章、參考資料................................. 36 | - |
| dc.description.tableofcontents | 第一章、序論................................. 1 第二章、相關研究................................. 3 2.1 推薦系統................................. 3 2.2 推薦方法................................. 4 2.3 音樂推薦系統................................. 5 2.4 音樂特徵擷取................................. 6 2.5 文字特徵擷取................................. 6 第三章、研究方法................................. 8 3.1 使用者特徵擷取（User Profile Extractor）................................. 10 3.1.1 音樂偏好管理（Music Profile Manager）................................. 10 3.1.2 文字偏好管理（Textual Profile Manager）................................. 19 3.1.3 相似度距離定義（Definition of Similarity Distance）................................. 21 3.2 推薦模組（Recommendation Module）................................. 21 3.2.1 特徵結合（Feature Combination）................................. 22 3.2.2 協同式推薦（Collaborative Recommendation）................................. 25 第四章、驗證方法................................. 26 4.1 使用者音樂偏好檔案之效能（Effectiveness of the Music Profile）......................... 26 4.2 評估使用者標籤以及使用者評論之品質（Evaluation the quality of User Tags and User Comment）................................. 28 4.3 推薦結果評估方法（The evaluation method of Recommendations）......................... 30 4.4 特徵結合方法之比較（Compare with Combination methods）................................. 31 第五章、結論................................. 35 第六章、參考資料................................. 36 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0967530361 | en_US |
| dc.subject (關鍵詞) | 音樂推薦 | zh_TW |
| dc.subject (關鍵詞) | 使用者分群 | zh_TW |
| dc.subject (關鍵詞) | 特徵切割 | zh_TW |
| dc.subject (關鍵詞) | music recommendation | en_US |
| dc.subject (關鍵詞) | user clustering | en_US |
| dc.subject (關鍵詞) | feature partition | en_US |
| dc.title (題名) | 基於音樂特徵以及文字資訊的音樂推薦 | zh_TW |
| dc.title (題名) | Music recommendation based on music features and textual information | en_US |
| dc.type (資料類型) | thesis | en_US |
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