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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 廖婉君(Wanjiun Liao) | |
| dc.contributor.author | Hao-Wei Luan | en |
| dc.contributor.author | 欒浩偉 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:37:49Z | - |
| dc.date.available | 2020-08-29 | |
| dc.date.copyright | 2017-08-29 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-07-31 | |
| dc.identifier.citation | [1] Xiaodan Song, Belle L Tseng, Ching-Yung Lin, and Ming-Ting Sun. Personalized recommendation driven by information flow. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 509–516. ACM, 2006. [2] Xujuan Zhou, Yue Xu, Yuefeng Li, Audun Josang, and Clive Cox. The state-of-theart in personalized recommender systems for social networking. Artificial Intelligence Review, 37(2):119–132, 2012. [3] Matthew Richardson and Pedro Domingos. Mining knowledge-sharing sites for viral marketing. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 61–70. ACM, 2002. [4] David Kempe, Jon Kleinberg, and Éva Tardos. Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137–146. ACM, 2003. [5] Wei Chen, Chi Wang, and Yajun Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1029–1038. ACM, 2010. [6] Manuel Gomez Rodriguez, Jure Leskovec, and Andreas Krause. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1019–1028. ACM, 2010. [7] Seth Myers and Jure Leskovec. On the convexity of latent social network inference. In Advances in Neural Information Processing Systems, pages 1741–1749, 2010. [8] Manuel G Rodriguez, David Balduzzi, and Bernhard Schölkopf. Uncovering the temporal dynamics of diffusion networks. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 561–568, 2011. [9] Liaoruo Wang, Stefano Ermon, and John E Hopcroft. Feature-enhanced probabilistic models for diffusion network inference. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 499–514. Springer, 2012. [10] Nan Du, Le Song, Hyenkyun Woo, and Hongyuan Zha. Uncover topic-sensitive information diffusion networks. In Proceedings of the sixteenth international conference on artificial intelligence and statistics, pages 229–237, 2013. [11] Ming-Hao Yang, Chung-Kuang Chou, and Ming-Syan Chen. Cluster cascades: Infer multiple underlying networks using diffusion data. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, pages 281–284. IEEE, 2014. [12] Senzhang Wang, Xia Hu, Philip S Yu, and Zhoujun Li. Mmrate: Inferring multiaspect diffusion networks with multi-pattern cascades. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1246–1255. ACM, 2014. [13] Qingbo Hu, Sihong Xie, Shuyang Lin, Wei Fan, and Philip S Yu. Frameworks to encode user preferences for inferring topic-sensitive information networks. In Proceedings of the 2015 SIAM International Conference on Data Mining, pages 442–450. SIAM, 2015. [14] Pei-Lun Liao, Chung-Kuang Chou, and Ming-Syan Chen. Uncovering multiple diffusion networks using the first-hand sharing pattern. In Proceedings of the 2016 SIAM International Conference on Data Mining, pages 63–71. SIAM, 2016. [15] David Krackhardt, N Nohria, and B Eccles. The strength of strong ties. Networks in the knowledge economy, page 82, 2003. [16] Mark S Granovetter. The strength of weak ties. American journal of sociology, 78(6):1360–1380, 1973. [17] Nicola Barbieri, Francesco Bonchi, and Giuseppe Manco. Topic-aware social influence propagation models. Knowledge and information systems, 37(3):555–584, 2013. [18] Chung-Kuang Chou and Ming-Syan Chen. Multiple factors-aware diffusion in social networks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 70–81. Springer, 2015. [19] Steven Diamond and Stephen Boyd. CVXPY: A Python-embedded modeling language for convex optimization. Journal of Machine Learning Research, 17(83):1–5, 2016. [20] B. O’Donoghue, E. Chu, N. Parikh, and S. Boyd. Conic optimization via operator splitting and homogeneous self-dual embedding. Journal of Optimization Theory and Applications, 169(3):1042–1068, June 2016. URL http://stanford.edu/~boyd/papers/scs.html. [21] B. O’Donoghue, E. Chu, N. Parikh, and S. Boyd. SCS: Splitting conic solver, version 1.2.6. https://github.com/cvxgrp/scs, April 2016. [22] Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, and Zoubin Ghahramani. Kronecker graphs: An approach to modeling networks. Journal of Machine Learning Research, 11(Feb):985–1042, 2010. [23] J Leskovec, L Backstrom, and J Kleinberg. Memetracker data, 2008. [24] Stanisław Osiński and Dawid Weiss. Carrot2: Design of a flexible and efficient web information retrieval framework. In International Atlantic Web Intelligence Conference, pages 439–444. Springer, 2005. [25] Ivan N Sanov. On the probability of large deviations of random variables. Technical report, North Carolina State University. Dept. of Statistics, 1958. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67563 | - |
| dc.description.abstract | 隨著使用者愈來愈樂意在社群網路中分享資料，商人也持續地從可獲取的鉅量資料中來對潛在消費者試著做分析。其中一個方式是經由觀察使用者頻繁分享的內容來推論他們的主題偏好，故個人化的廣告得以實施；另一種想法是建模探討使用者之間的影響力關係，以達到最大程度的病毒式行銷。本論文所提出在資訊層疊上的研究可以同時適用於前述兩項應用。其關鍵的概念是，一個資訊是否會被該資訊的接收者所分享，端看於該資訊傳送者與接收者之間的影響力關係，以及接收者對於該資訊的主題偏好。在這篇論文中，我們提出一個聯合優化問題以帶標題的資訊層疊推論影響力及使用者偏好，並展現其相對於相關文獻上的優勢。在合成資料及真實資料上的實驗評估中，皆顯示了本模型對於未看過的資訊層疊資料有更好的預測能力。 | zh_TW |
| dc.description.abstract | As data shared between users keep exploding in online social network, marketers keep trying to get more information about their potential customers over the available big data. One way is to infer the preference of users from their sharing/visiting content so that proper advertisement could be given, and another idea is to model the influence between users to maximize the viral marketing power. We argue that our approach to the study of information cascades could satisfy both applications at the same time. The main point here is whether a piece of information will be shared by a receiver is jointly determined by (a) the influence between the sender and the receiver, and (b) the receiver's preference to the particular topic of the received information. In this thesis, we propose a joint optimization problem on inferring influence and users' preference through topic-sensitive information cascades, and demonstrate several advantages over the related literature. Experiments with synthetic data and real data show that out model has better predicting power on unseen information cascades. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:37:49Z (GMT). No. of bitstreams: 1 ntu-106-R04942033-1.pdf: 1330331 bytes, checksum: b28b8dd96689ee8d600e7226d59c3ce5 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審訂書 誌謝 i 中文摘要 ii Abstract iii List of Figures vi List of Tables vii 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Influence Maximization . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Diffusion Network Inference . . . . . . . . . . . . . . . . . . . . . . . . 2 1.4 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.6 Organization of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Review of Diffusion Network Inference 8 2.1 Information Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Likelihood of a Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Maximizing Likelihood of Cascades . . . . . . . . . . . . . . . . . . . . 12 3 System Model 13 3.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Likelihood of a Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Joint Function Expression . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.4 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.5 Proposed Solving Techniques . . . . . . . . . . . . . . . . . . . . . . . . 18 3.5.1 Iterative method . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.5.2 Approximation method . . . . . . . . . . . . . . . . . . . . . . . 19 4 Experiments on Synthetic Data 20 4.1 Compared Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2.1 Mean Average Error . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2.2 Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2.3 Precision-Recall Curve . . . . . . . . . . . . . . . . . . . . . . . 24 4.3 Synthetic Data Generation . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.4 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4.1 Mean Average Error . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4.2 In-sample Likelihood . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4.3 Out-sample Likelihood . . . . . . . . . . . . . . . . . . . . . . . 27 4.4.4 Precision-Recall Curve . . . . . . . . . . . . . . . . . . . . . . . 29 4.5 Variable Training Cascades . . . . . . . . . . . . . . . . . . . . . . . . . 29 5 Experiments on Real Data 32 5.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.2 Experiment Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2.1 In-sample Likelihood . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2.2 Out-sample Likelihood . . . . . . . . . . . . . . . . . . . . . . . 36 5.2.3 Precision-Recall Curve . . . . . . . . . . . . . . . . . . . . . . . 38 6 Conclusion 40 A Convexity of Subproblems in Iterative Method 42 B Justification of Out-sample Likelihood 44 Bibliography 47 | |
| dc.language.iso | en | |
| dc.subject | 網路推論 | zh_TW |
| dc.subject | 社群網路 | zh_TW |
| dc.subject | 資訊散播 | zh_TW |
| dc.subject | 影響力估計 | zh_TW |
| dc.subject | Information Diffusion | en |
| dc.subject | Network Inference | en |
| dc.subject | Influence Estimation | en |
| dc.subject | Social Network | en |
| dc.title | 以帶標題資訊層疊推論影響力及偏好之方法 | zh_TW |
| dc.title | Influence and Preference Inference through Topic-sensitive Information Cascades | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 張正尚(Cheng-Shang Chang) | |
| dc.contributor.oralexamcommittee | 林宗男,陳銘憲,楊得年 | |
| dc.subject.keyword | 資訊散播,網路推論,影響力估計,社群網路, | zh_TW |
| dc.subject.keyword | Information Diffusion,Network Inference,Influence Estimation,Social Network, | en |
| dc.relation.page | 50 | |
| dc.identifier.doi | 10.6342/NTU201702109 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-07-31 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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