Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/127217| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 蔡銘峰 | zh_TW |
| dc.contributor.advisor | Tsai, Ming-Feng | en_US |
| dc.contributor.author | 楊昇芳 | zh_TW |
| dc.contributor.author | Yang, Sheng-Fang | en_US |
| dc.creator | 楊昇芳 | zh_TW |
| dc.creator | Yang, Sheng-Fang | en_US |
| dc.date | 2019 | en_US |
| dc.date.accessioned | 2019-11-06T07:27:40Z | - |
| dc.date.available | 2019-11-06T07:27:40Z | - |
| dc.date.issued | 2019-11-06T07:27:40Z | - |
| dc.identifier | G0106753011 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/127217 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 資訊科學系 | zh_TW |
| dc.description | 106753011 | zh_TW |
| dc.description.abstract | 近年來大數據以及機器學習技術的蓬勃發展，推薦系統被廣泛應用於各種實務上，而音樂串流系統中的音樂推薦也變成一項具有挑戰性的工作，尤其在各個不同市場中，群體的聆聽習慣也會有所不同。因此，我們使用了異質性網路表示法學習（ Heterogeneous Information Network Embedding ），可以將網路中不同類型之節點投影於低維度向量空間中，並基於此空間來完成後續相關之音樂推薦工作。又因對於新開發市場，用戶與歌曲聆聽紀錄等互動的資訊極為稀少且會因少數用戶而影響整體推薦的傾向，這便稱為資料的「稀疏性」問題，而資料的稀疏性通常是實務上一個很具有挑戰性的任務，其對於推薦系統整體的推薦效果影響是很巨大的。於是，本論文提出了一個基於異質性網路表示法學習的音樂推薦系統，透過加入網路資訊較為豐富的市場作為輔助來幫助改進新開發市場之推薦效果。 | zh_TW |
| dc.description.abstract | In recent years, big data and machine learning technology have been rapidly growing, and recommendation systems have been widely used in various real-world applications, such as music recommendation in music streaming services. However, for different domains, the recommneder systems will be different, because of the distinct user behavior data. Therefore, this thesis aims to use Heterogeneous Information Network Embedding to project the nodes in a network/domain into another network/domain on the basis of the low-dimension representations of the nodes. Therefore, this paper proposes a cross-domain music recommendation approach based on heterogeneous information network representation learning, the idea of which is to enrich the new domain/market data by using a well developed domain/market. | en_US |
| dc.description.tableofcontents | 致謝 1\n中文摘要 2\nAbstract 3\n第一章 緒論 1\n1.1 前言 1\n1.2 研究目的 2\n第二章 相關文獻探討 4\n2.1 網路表示法學習 4\n2.2 推薦系統 5\n2.3 遷移式學習 6\n第三章 研究方法 8\n3.1 問題定義 8\n3.2 異質性網路建圖 8\n3.3 建立超連結圖譜 10\n3.4 網路表示法學習 12\n3.4.1 Deepwalk 12\n3.4.2 Large-Scale Information Network Embedding 12\n3.4.3 HeterogeneousPreferenceEmbedding 14\n3.5 推薦系統 14\n第四章 實驗結果與討論 16\n4.1 資料集 16\n4.2 實驗設定 17\n4.3 評估標準 19\n4.4 實驗結果分析與討論 21\n4.4.1 準確率表現 21\n4.4.2 召回率表現 21\n4.4.3 平均準確率均值表現 22\n4.4.4 新穎度表現 23\n4.5 實例分析 24\n4.5.1 實例分析-以推薦系統為例 24\n4.5.2 實例分析-以網路表示法學習為例 24\n第五章 結論 29\n參考文獻 30 | zh_TW |
| dc.format.extent | 1864696 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0106753011 | en_US |
| dc.subject | 網路表示法 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 特徵值學習 | zh_TW |
| dc.subject | 遷移學習 | zh_TW |
| dc.subject | Network embedding | en_US |
| dc.subject | Recommendation systems | en_US |
| dc.subject | Feature learning | en_US |
| dc.subject | Transfer learning | en_US |
| dc.title | 基於超連結圖譜表示法學習之跨領域音樂推薦演算法 | zh_TW |
| dc.title | Cross-domain music recommendation based on superhighway graph embedding | en_US |
| dc.type | thesis | en_US |
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In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI ’09, pages 452–461, Ar- lington, Virginia, United States, 2009. AUAI Press.\n[24] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. pages 175–186. ACM Press, 1994.\n[25] J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. Line: Large-scale infor- mation network embedding. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pages 1067–1077, Republic and Canton of Geneva, Switzerland, 2015. International World Wide Web Conferences Steering Committee.\n[26] Y. Zhang, Q. Ai, X. Chen, and W. B. Croft. Joint representation learning for top- n recommendation with heterogeneous information sources. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, pages 1449–1458, New York, NY, USA, 2017. ACM. | zh_TW |
| dc.identifier.doi | 10.6814/NCCU201901201 | en_US |
| item.grantfulltext | restricted | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| item.openairetype | thesis | - |
| item.cerifentitytype | Publications | - |
| item.fulltext | With Fulltext | - |
| Appears in Collections: | 學位論文 | |
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| 301101.pdf | 1.82 MB | Adobe PDF2 | View/Open |
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