音樂歌單是由一組多首不同元素、風格的音樂所組成的，它包含了編輯者的個人品味以及因應主題、目的性產生而成。我們可以透過樂曲的律動、節奏、歌曲的主題精神，進而編輯一個相應契合的系列歌曲。當今的音樂收聽市場主要是在網路串流平台上進行隨時、隨地的聆聽，主要的平台有Spotify、Apple Music 以及KKBOX。各家業者不單只是提供使用者歌曲的搜索、單曲的聆聽，更提供訂閱專業歌單編輯者的歌單訂閱服務，甚至是讓一般的使用者參與歌單自訂編輯的過程。然而如何在有限的時間內針對使用者的聆聽習慣去介紹平台上豐富的音樂資源是個很大的挑戰。上述的過程我們稱之為推薦，而當前的音樂推薦研究大多是在對使用者進行相關歌曲的推薦，鮮少能進一步在更抽象層次上的歌單上進行推薦。這邊我們就此一推薦應用提供嵌入式向量表示法學習模型，在有著使用者、歌曲、歌單的異質性社交網路上，對使用者進行歌單的推薦。為了能有效的學習出歌單推薦的模型，我們更將使用者、歌單和歌曲的異質性圖形重組成二分圖（bipartite graph）， 並在此圖形的邊上賦予不等的權重，此一權重是基於使用者隱式反饋獲得的。接著再透過隨機漫步（random walk），根據邊上的權值進行路徑的抽樣選取，最後再將路徑上經過的節點進行嵌入式向量表示法的學習。我們使用歐幾里德距離計算各節點表示法的鄰近關係，再將與使用者較為相關的歌單推薦給使用者。實驗驗證的部分，我們蒐集KKBOX 兩年份的資料進行模型訓練並進行推薦，並將推薦的結果與使用者所喜愛的歌單進行準確度（Precision）評估， 結果證實所得到的推薦效果較一般熱門歌單的推薦來的好，且為更具個人化的歌單推薦。 / Music playlist is crafted with a series of songs, in which the playlist creator has controlled over the vibe, tempo, theme, and all the ebbs and flows that come within the playlist. To provide a personalization service to users and discover suitable playlists among lots of data, we need an effective way to achieve this goal. In this paper, we modify a representation learning method for learning the representation of a playlist of songs, and then use the representation for recommending playlists to users. While there have been some well-known methods that can model the preference between users and songs, little has been done in the literature to recommend music playlists. In light of this, we apply DeepWalk, LINE and HPE to a user-song-playlist network. To better encode the network structure, we separate user, song, and playlist nodes into two different sets, which are grouped by the user and playlist set and song as the other one. In the bipartite graph, the user and playlist node are connected to their joint songs. By adopting random walks on the constructed graph, we can embed users and playlists via the common information between each other. Therefore, users can discover their favorite playlists through the learned representations. After the embedding process, we then use the learned representations to perform playlist recommendation task. Experiments conducted on a real-world dataset showed that these embedding methods have a better performance than the popularity baseline. In addition, the embedding method learns the informative representations and brings out the personal recommendation results.