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系統識別號 U0026-0408201617424500
論文名稱(中文) 基於隱含情境因素之行動音樂推薦系統
論文名稱(英文) A Mobile Music Recommender System Based on Latent Context Factors
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 104
學期 2
出版年 105
研究生(中文) 翁世昕
研究生(英文) Shih-Hsin Wong
學號 R76031113
學位類別 碩士
語文別 中文
論文頁數 41頁
口試委員 指導教授-劉任修
口試委員-翁慈宗
口試委員-王惠嘉
口試委員-蔡青志
中文關鍵字 音樂推薦  情境感知推薦系統  隱含情境因素  音樂資訊檢索 
英文關鍵字 Music Recommender System  Context-Aware Recommender System  Latent Context Factor  Music Information Retrieval 
學科別分類
中文摘要 網路音樂平台快速發展使得使用者回饋資訊更容易的被蒐集,造成了資訊過載的現象,音樂推薦系統的重要性日益漸增。在協同過濾法(Collaborative Filtering)的推薦演算法中,基於模型式方法(Model-Based Approach)除了預測階段速度快,亦擁有不錯的推薦結果, 然而當新歌曲進入系統後,系統並沒有足夠的資料進行分析,造成了冷開始的問題(Cold Start)。 不同於協同過濾,內容基礎式過濾法(Content-Based Filtering) 則是加入額外對於歌曲音訊內容的描述,有能力處理由新歌曲所造成的冷開始問題,但推薦結果通常較前者差。 為了同時具備對於兩種情況的處理能力,混合式過濾法 (Hybrid Methods)結合了前述方法滿足了這樣的需求。
本論文延續同時考量使用者回饋資料和音訊內容的音樂推薦的研究,加入環境資料設計了行動情境感知式推薦系統(Context-Aware Recommender System),能夠依據使用者當前所在的環境給予推薦結果,基於假設使用者會在不同環境下有不同喜好,將提高推薦結果的準確性。 一旦本推薦模型能夠分析了解使用者在不同環境下的喜好,就可以自動建立播放清單,在使用者不能手動選擇播放的歌曲時仍能夠藉由此推薦模型聆聽當下喜好的歌曲。 實驗資料集是來自於手機中的程式自動蒐集使用者回饋資訊和環境資料等等,目的是避免影響使用者實際聆聽音樂的習慣,以確保資料的真實性。 本論文所提出之隱含情境因子推薦模型(Latent Context Factors-Recommender Model )將同時考量使用者回饋資訊、歌曲內容和對應的情境資料,除了有能力依照當前環境給予推薦外,亦保有基於模型式方法的特性。 而根據本論文的實驗結果顯示本論文提出的推薦模型相較於Yoshii et al. (2008)和Su et al. (2010)所提出的方法為佳,證明本模型擁有一定水準的推薦正確率,且在模型中考環境相較於未考慮環境有些許的改善。
英文摘要 In this thesis, a new music recommender system called the Latent Context Factors Recommender Model is proposed.
The goal is to learn which kind of music users like in different contexts.
To reach the goal, we implement a smart-phone application to collect various data regarding music listening habits.
Users who agree to participate in this experiment do not need to manually report.
Instead, our program automatically collects the required data.
The collected record includes the user's ID, the title of the audio file, and the environmental attributes measured by the smart-phone's sensors.
These sensory attributes are helpful for inferring which environment the user was in while listening to the music.
Apart from the environment, audio content is also important to our study.
We analyze audio files and timbre attributes to characterize the content of the audio files.
Based on the collected data, our model is able to estimate occurrence probabilities of the users in different latent contexts, and to learn each user's preferences in those contexts.
According to the results of our analysis, recommendations are provided to users.
Because users may select songs according to what they are doing, estimating a user's probability of appearing in a given context is helpful to learning their preferences.
Results of several measures show that our recommender model has greater accuracy than other models.
However, the difference between the results with and without considering environmental attributes is not significant.
論文目次 摘要 i
EXTENDED ABSTRACT ii
致謝 v
目錄 vi
表目錄 viii
圖目錄 ix
1 緒論 1
1.1 背景及動機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 貢獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 論文架構 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 相關文獻探討 4
2.1 推薦系統 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 協同過濾法(Collaborative Filtering) . . . . . . . . . . . . . 5
2.1.2 內容式過濾法(Content-Based Filtering) . . . . . . . . . . . 9
2.1.3 混合過濾法(Hybrid Methods) . . . . . . . . . . . . . . . . 10
2.1.4 情境感知式推薦系統(Context-Aware Recommender System) 11
2.1.5 推薦方法的比較 . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 音樂音訊內容之處理方法 . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 特徵擷取 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 預處理方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3 研究方法 16
3.1 問題描述及模型介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 符號介紹 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3 資料預處理 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.4 模型學習過程 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.5 歌曲推薦 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 實驗流程及結果探討 26
4.1 資料集來源 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2 驗證方法及衡量測度 . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.3 實驗流程 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.4 實驗結果探討 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
5 結論及未來發展 37
參考文獻 39
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