電機資訊學院: 資訊網路與多媒體研究所指導教授: 鄭卜壬林家瑞Lin, Chia-JuiChia-JuiLin2017-03-062018-07-052017-03-062018-07-052016http://ntur.lib.ntu.edu.tw//handle/246246/275963在現在的世界裡有數百萬首的歌曲，有些雲端的音樂服務像是 Youtube、Spotify、Apple Music 等收集了許多的歌曲並提供給消費者一 個友善的介面，隨著這些服務的出現，更能夠幫助消費者探索這大量 的音樂選擇，而為了滿足消費者的偏好，一個好的推薦歌曲方法是必 要的，在學術上有許多的論文在探討音樂推薦相關的議題。 而為了評斷不同的方法的表現好壞，一般來說都會使用評估指標來 評量，在資訊檢索領域中有許多不同的評估指標，但大部分的評估指 標都是非平滑的，這會造成最佳化的損失函數與評估指標不匹配，為 了克服這個問題，我們引進排序學習來消除不匹配。 在本論文中，我們將每一首歌曲以 d 個維度的隱向量表示，且使用 了一個測量排序被稱為 NDCG 的評估指標來作為評估，給定一個歌曲 序列，我們的模型會回傳一個預測出現在此序列後下一首的排序，實 驗顯示我們的模型表現比其他方法還好，這表示我們模型的排序預測 跟實際排序更為接近。In the present, there are millions of songs in the world. Some cloud-based music services (e.g. Youtube, Spotify and Apple Music) gather songs and provide a friendly user interface to consumers. As the appearance of the ser- vices, it can help consumers to explore the large set of songs. In order to satisfy the preference of consumers, a good recommendation method is nec- essary. There are many scholarly works focused on the related topic in music recommendation. To check the performance of different models are good or not, the evaluation metric is used in general. There are a lots of different met- rics in the information retrieval field. However, most of them are non-smooth. This cause a problem that there are a mismatch between the optimizing cost function and the evaluation metric. To overcome the problem, we introduce learning to rank to help to eliminate the mismatch. In this work, we embed each song with a d-dimension vector and use a ranking measure NDCG (Nor- mailized Discounted Cumulative Gain) as our evaluation metric. Given a song sequence, our model predict a ranking of next song right after the sequence. The experiments show that our model perform better than others. It indicates the ranking prediction of our model is more approximating the real ranking.11854730 bytesapplication/pdf論文公開時間: 2026/12/31論文使用權限: 同意有償授權(權利金給回饋學校)音樂清單推薦排序學習隱表示非平滑損失函數Music PlaylistsRecommendation SystemLearning to RankSong EmbeddingNon-smooth Cost Functionthesis10.6342/NTU201601641http://ntur.lib.ntu.edu.tw/bitstream/246246/275963/1/ntu-105-R03944009-1.pdf