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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
    Lai, Tung-Sheng
    Keywords: 推薦系統
    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.
    Reference: [1] http://www.cp.jku.at/datasets/LFM-1b/
    [2] M. Schedl. The LFM-1b Dataset for Music Retrieval and Recommendation. In Proceedings of the ICMR. 2016.
    [3] A. Poddar, E. Zangerle, and Y. Yang. #nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems. In Proceedings of the Sound and Music Computing Conf. 2018.
    [4] T. Mikolov, K. Chen, G.Corrado, and J. Dean. Efficient Estimation of Word Representations in Vector Space. ICLR Workshop. 2013.
    [5] Q. Le, T. Mikolov. Distributed Representations of Sentences and Documents. In Proceedings of the ICML. 2014.
    [6] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale Information Network Embedding. In Proceedings of the 24th International Conference on World Wide Web. 2015.
    [7] D. Wang, S. Deng, X. Zhang, and G. Xu. Learning Music Embedding with Metadata for Context Aware Recommendation. In Proceedings of the ICMR. 2016.
    [8] J. Tang and K. Wang. Personalized Top-n Sequential Recommendation Via Convolutional Sequence Embedding. In Proceedings of the WSDM. 2018.
    [9] H. Chen, B. Perozzi, R. Al-Rfou, and S. Skiena. A Tutorial on Network Embeddings. arXiv preprint arXiv:1808.02590. 2018.
    [10] B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online Learning of Social Representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014.
    [11] R. Salakhutdinov and A. Mnih. Probabilistic Matrix Factorization. In Advances in Neural Information Processing Systems, volume 20. 2008.
    [12] Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. IEEE Computer, 42(8):30–37, 2009.
    [13] Y. Hu, Y. Koren, and C. Volinsky. Collaborative Filtering for Implicit Feedback Datasets. In IEEE International Conference on Data Mining. 2008.
    [14] O. Barkan and N. Koenigstein. Item2vec: Neural Item Embedding for Collaborative Filtering. arXiv preprint arXiv:1603.04259. 2016.
    [15] M. Grbovic and H. Cheng. Real-time Personalization Using Embeddings for Search Ranking at Airbnb. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018.
    [16] H. Zamani, M. Schedl, P. Lamere, and C. Chen. An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation. arXiv preprint arXiv:1810.01520. 2018.
    [17] https://www.spotify.com/us/discoverweekly/
    [18] L. van der Maaten and G. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9:2579-2605. 2008.
    [19] L. van der Maaten. Accelerating t-SNE Using Tree-based Algorithms. Journal of machine learning research. 2014.
    [20] O. Levy, Y. Goldberg, and I. RamatGan. Linguistic Regularities in Sparse and Explicit Word Representations. CoNLL-2014.
    [21] T. Bolukbasi, K. Chang, J. Zou, V. Saligrama, and A. Kalai. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In Advances in Neural Information Processing Systems. 2016.
    [22] B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk. Session-Based Recommendations with Recurrent Neural Networks. arXiv preprint arXiv:1511.06939. 2015.
    [23] Y. Kim. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882. 2014.
    [24] https://en.wikipedia.org/wiki/Shoegazing#History
    [25] S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing Personalized Markov Chains for Next-Basket Recommendation. In International Conference on World Wide Web. ACM, 811–820. 2010.
    [26] S. Zhang, L. Yao, and A. Sun. Deep Learning Based Recommender System: A survey and New Perspectives. arXiv preprint arXiv:1707.07435, 2017.
    [27] Z. Cheng, J. Shen, L. Zhu, M. Kankanhalli, and L. Nie. Exploiting Music Play Sequence for Music Recommendation. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017.
    [28] https://github.com/tangjianpku/LINE
    [29] R. R ̌ehu ̇r ̌ek and P. Sojka. Software Framework for Topic Modelling with Large Corpora. In LREC, 2010.
    [30] C. Chen, M. Tsai, Y. Lin, and Y. Yang. Query-based music recommendations via preference embedding. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 79–82. ACM, 2016.
    Description: 碩士
    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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