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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 鄭卜壬(Pu-Jen Cheng) | |
dc.contributor.author | Hsin-Ping Chen | en |
dc.contributor.author | 陳心萍 | zh_TW |
dc.date.accessioned | 2021-05-13T06:40:33Z | - |
dc.date.available | 2019-07-27 | |
dc.date.available | 2021-05-13T06:40:33Z | - |
dc.date.copyright | 2017-07-27 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-07-20 | |
dc.identifier.citation | [1] A. Ahmed, L. Hong, and A. J. Smola. Hierarchical geographical modeling of user locations from social media posts. In Proceedings of the 22Nd International Conference on World Wide Web, WWW ’13, pages 25–36, New York, NY, USA, 2013. ACM.
[2] A. Ahmed, N. Shervashidze, S. Narayanamurthy, V. Josifovski, and A. J. Smola. Distributed large-scale natural graph factorization. In Proceedings of the 22Nd International Conference on World Wide Web, WWW ’13, pages 37–48, New York, NY, USA, 2013. ACM. [3] M.Belkin and P.Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput., 15(6):1373–1396, June 2003. [4] S. Cao, W. Lu, and Q. Xu. Grarep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM ’15, pages 891–900, New York, NY, USA, 2015. ACM. [5] S. Chang, W. Han, J. Tang, G.-J. Qi, C. C. Aggarwal, and T. S. Huang. Heterogeneous network embedding via deep architectures. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, pages 119–128, New York, NY, USA, 2015. ACM. [6] C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang. Query-based music recommendations via preference embedding. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pages 79–82, New York, NY, USA, 2016. ACM. [7] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9:1871–1874, 2008. [8] A. Grover and J. Leskovec. Node2vec: Scalable feature learning for networks. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 855–864, New York, NY, USA, 2016. ACM. [9] C. Guo and X. Liu. Automatic feature generation on heterogeneous graph for music recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’15, pages 807–810, New York, NY, USA, 2015. ACM. [10] F. M. Harper and J. A. Konstan. The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst., 5(4):19:1–19:19, Dec. 2015. [11] Y. Jacob, L. Denoyer, and P. Gallinari. Learning latent representations of nodes for classifying in heterogeneous social networks. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM ’14, pages 373– 382, New York, NY, USA, 2014. ACM. [12] Y. Juan, Y. Zhuang, W.-S. Chin, and C.-J. Lin. Field-aware factorization machines for ctr prediction. In Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pages 43–50, New York, NY, USA, 2016. ACM. [13] X. Liu, Y. Yu, C. Guo, and Y. Sun. Meta-path-based ranking with pseudo relevance feedback on heterogeneous graph for citation recommendation. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM ’14, pages 121–130, New York, NY, USA, 2014. ACM. [14] C.Meng, R.Cheng, S.Maniu, P.Senellart, and W.Zhang. Discovering meta-paths in large heterogeneous information networks. In Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pages 754–764, Republic and Canton of Geneva, Switzerland, 2015. International World Wide Web Conferences Steering Committee. [15] 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, KDD ’14, pages 701–710, New York, NY, USA, 2014. ACM. [16] S. Rendle and L. Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM ’10, pages 81–90, New York, NY, USA, 2010. ACM. [17] Y. Sun, J. Han, C. C. Aggarwal, and N. V. Chawla. When will it happen?: Relationship prediction in heterogeneous information networks. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM ’12, pages 663–672, New York, NY, USA, 2012. ACM. [18] J. Tang, T. Lou, J. Kleinberg, and S. Wu. Transfer learning to infer social ties across heterogeneous networks. ACM Trans. Inf. Syst., 34(2):7:1–7:43, Apr. 2016. [19] J. Tang, M. Qu, and Q. Mei. Pte: Predictive text embedding through large-scale heterogeneous text networks. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, pages 1165–1174, New York, NY, USA, 2015. ACM. [20] 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, WWW ’15, pages 1067–1077, Republic and Canton of Geneva, Switzerland, 2015. International World Wide Web Conferences Steering Committee. [21] D. Wang, P. Cui, and W. Zhu. Structural deep network embedding. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 1225–1234, New York, NY, USA, 2016. ACM. [22] J. Yang and J. Leskovec. Defining and evaluating network communities based on ground-truth. In Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, MDS ’12, pages 3:1–3:8, New York, NY, USA, 2012. ACM. [23] X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM ’14, pages 283–292, New York, NY, USA, 2014. ACM. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2465 | - |
dc.description.abstract | 學習網路表示法技術是目前很熱門的研究主題,此技術從複雜的網路結構學習出低維度的表示法代表節點,此表示法除了保留網路結構的關係外,讓網路中的節點能做向量的運算,對於後續的機器學習問題提供較高的基礎,像是多分類問題、預測問題和推薦問題。 但是目前的學習網路表示法技術並不能很好的應用在異質性的網路中,因為異質性網路包含不同類別的節點與多種類別關係,學習後的表示法來自於不同的向量空間不能比較。基於這個原因,本篇研究將領域感知的概念應用在學習表示法技術,希望利用這種概念改善學習網路表示法技術在異質性網路中無法學習出可以比較的向量問題。本研究也應用在多個生活中的資料集,實驗證明,本研究不只能保留異質性網的關係,對於後續的機器學習問題也能有好的成果。 | zh_TW |
dc.description.abstract | Network embedding is used for extracting the feature representations of a network and benefits many machine learning tasks, such as classification, link prediction, etc. This model embeds the interactions among the vertices into the low-dimension representations, which greatly preserve the relations of the vertices. However, to simplify the learning procedure, most previous work treats all the vertices as the same type and thus ignores the interaction type of two vertices in different fields. In the light of this, we propose a field-aware network embedding model which can separately embed the distinct kinds of the interactions into the learned representations. Our experimental results show that integrating such field-aware information indeed improves the
performance of the state-of-the-art network embedding algorithm. | en |
dc.description.provenance | Made available in DSpace on 2021-05-13T06:40:33Z (GMT). No. of bitstreams: 1 ntu-106-R04944008-1.pdf: 1729703 bytes, checksum: 77ee19895d27acafd316d5355d25f948 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii Abstract iv 1 Introduction 1 2 Related Works 5 2.1 The idea of field-aware........................... 5 2.2 Network Embedding algorithms ...................... 6 2.3 Heterogeneous Networks.......................... 7 3 Methodology 8 3.1 Problem Definition ............................. 8 3.2 Field-aware embedding structure...................... 9 3.3 Model Description ............................. 11 3.4 Model Optimization............................. 12 4 Experiments 14 4.1 Dataset ................................... 14 4.2 Compared Algorithms ........................... 16 4.3 Evaluation Metrics ............................. 16 4.4 Parameter Settings ............................. 17 4.5 Experiment Results............................. 18 4.5.1 Network Reconstruction ...................... 18 4.5.2 Multi-label Classification...................... 20 4.5.3 Link Prediction/ Item Recommendations . . . . . . . . . . . . . 21 4.5.4 Documents Classification ..................... 22 4.5.5 Experiment Results Summary ................... 22 5 Discussion 23 5.1 Parameter Sensitive............................. 23 5.1.1 Performance w.r.t. #Samples.................... 23 5.1.2 Performance w.r.t. Dimension ................... 23 5.1.3 Performance w.r.t. NetworkSparsity. . . . . . . . . . . . . . . . 24 5.2 Dimension of Fields............................. 25 6 Conclusion 27 Bibliography 28 | |
dc.language.iso | en | |
dc.title | 學習基於異質性網路的領域感知網路表示法 | zh_TW |
dc.title | Field-aware Network Embedding on Heterogeneous Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳信希(Hsin-Hsi Chen),彭文志(Wen-Chih Peng),蔡銘峰(Ming-Feng Tsai) | |
dc.subject.keyword | 網路表示法,特徵學習,領域感知式模型,異質性網路,類神經網路, | zh_TW |
dc.subject.keyword | Network embedding,Feature learning,Field-aware model,Heterogeneous Networks,Neural Network, | en |
dc.relation.page | 31 | |
dc.identifier.doi | 10.6342/NTU201701689 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2017-07-20 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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