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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/124120
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/124120


    Title: 應用Embedding於音樂播放推薦
    Application of embedding in music recommendation
    Authors: 賴東昇
    Lai, Tung-Sheng
    Contributors: 翁久幸
    Weng, Chiu-Hsing
    賴東昇
    Lai, Tung-Sheng
    Keywords: 推薦系統
    音樂推薦
    Embedding
    Recommendation
    Date: 2019
    Issue Date: 2019-07-01 10:43:21 (UTC+8)
    Abstract: Embedding為一種學習出目標之向量表示的方法。透過類神經網路或其他模型架構,Embedding能學習出優良的向量表示,並被廣泛用於文字分析、社群網路、推薦系統等領域。本論文使用Word2vec與LINE兩種embedding方法,透過序列化之音樂播放紀錄學習出使用者與音樂之向量表示,並檢視其性質。接著,我們結合兩者,同時考慮使用者之長期偏好與當下播放歌曲之性質,將其用於使用者之下一首歌曲、演唱者預測,並取得了不錯的準確率。研究顯示embedding方法可用於學習序列化資料之資訊,除了能呈現音樂之間的相似關係外,亦可用於音樂推薦之任務中。
    Embedding is a method used for learning vector representation of target objects. With neural network or other model structure, embedding is able to learn well vector representation and is used in text analysis, social network, recommendation system and other fields. In this paper, we use two embedding method, Word2vec and LINE, along with music listening log data to learn the embedding of users and songs. We first show that the music embedding is able to preserve the genre similarity. Further, we combine user’s long term preference and current listening-session preference learned by embedding to conduct next n song and next n artist prediction. Result shows that embedding methods can be used in music recommendations.
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    Description: 碩士
    國立政治大學
    統計學系
    106354002
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106354002
    Data Type: thesis
    DOI: 10.6814/NCCU201900087
    Appears in Collections:[統計學系] 學位論文

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