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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李瑞庭(Anthony J.T. Lee) | |
dc.contributor.author | Yu-Ting Lin | en |
dc.contributor.author | 林郁婷 | zh_TW |
dc.date.accessioned | 2023-03-19T21:21:58Z | - |
dc.date.copyright | 2022-08-23 | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-07-19 | |
dc.identifier.citation | [1] M. De Veirman, V. Cauberghe, L. Hudders, Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude, International Journal of Advertising. 36 (2017) 798–828. https://doi.org/10.1080/02650487.2017.1348035. [2] R.R. Mallipeddi, S. Kumar, C. Sriskandarajah, Y. Zhu, A framework for analyzing influencer marketing in social networks: Selection and scheduling of influencers, Management Science. (2021) 1–30. https://doi.org/10.1287/mnsc.2020.3899. [3] O.L. Vyatkina, The impact of influencer marketing on the global economy, European Proceedings of Social and Behavioural Sciences. 79 (2020) 1–1576. https://doi.org/10.15405/epsbs.2020.03.187. [4] M. Gan, H. Zhang, DeepFusion: Fusing user-generated content and item raw content towards personalized product recommendation, Complexity. 2020 (2020) 1–12. https://doi.org/10.1155/2020/4780191. [5] S. Kumar, K. De, P.P. Roy, Movie recommendation system using sentiment analysis from microblogging data, IEEE Transactions on Computational Social Systems. 7 (2020) 915–923. https://doi.org/10.1109/TCSS.2020.2993585. [6] L. Zheng, N. Guo, W. Chen, J. Yu, D. Jiang, Sentiment-guided sequential recommendation, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020: pp. 1957–1960. https://doi.org/10.1145/3397271.3401330. [7] C.-C. Hung, Y.-C. Huang, J. Hsu, D.K.-C. Wu, Tag-based user profiling for social media recommendation, in: Proceedings of Workshop on Intelligent Techniques for Web Personalization and Recommender Systems at AAAI., 2008: pp. 49–55. [8] M. Pennacchiotti, A.-M. Popescu, A machine learning approach to Twitter user classification, Proceedings of the International AAAI Conference on Web and Social Media. 5 (2011) 281–288. [9] L. Wu, C. Quan, C. Li, Q. Wang, B. Zheng, X. Luo, A context-aware user-item representation learning for item recommendation, ACM Transactions on Information Systems. 37 (2019) 22:1-22:29. https://doi.org/10.1145/3298988. [10] M. Bertini, A. Ferracani, R. Papucci, A. Del Bimbo, Keeping up with the influencers: Improving user recommendation in Instagram using visual content, in: Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 2020: pp. 29–34. https://doi.org/10.1145/3386392.3397594. [11] P. Lovato, M. Bicego, C. Segalin, A. Perina, N. Sebe, M. Cristani, Faved! Biometrics: Tell me which image you like and I’ll tell you who you are, IEEE Transactions on Information Forensics and Security. 9 (2014) 364–374. https://doi.org/10.1109/TIFS.2014.2298370. [12] Q. You, S. Bhatia, J. Luo, A picture tells a thousand words—About you! User interest profiling from user generated visual content, Signal Processing. 124 (2016) 45–53. https://doi.org/10.1016/j.sigpro.2015.10.032. [13] J. Zhou, R. Albatal, C. Gurrin, Applying visual user interest profiles for recommendation & personalisation, in: Proceedings of the 22nd International Conference on Multimedia Modeling, 2016: pp. 361–366. [14] A. Elwood, A. Gasparin, A. Rozza, Ranking micro-influencers: A novel multi-task learning and interpretable framework, in: Proceedings of the IEEE International Symposium on Multimedia, 2021: pp. 130–137. https://doi.org/10.1109/ISM52913.2021.00030. [15] T. Gan, S. Wang, M. Liu, X. Song, Y. Yao, L. Nie, Seeking micro-influencers for brand promotion, in: Proceedings of the 27th ACM International Conference on Multimedia, 2019: pp. 1933–1941. https://doi.org/10.1145/3343031.3351080. [16] T. Sweet, A. Rothwell, X. Luo, Machine learning techniques for brand-influencer matchmaking on the Instagram social network, Computing Research Repository. 1901.05949 (2019). [17] Y. Zhang, X. Wang, Y. Sakai, T. Yamasaki, Measuring similarity between brands using followers’ post in social media, in: Proceedings of the ACM Multimedia Asia, 2019: pp. 1–6. https://doi.org/10.1145/3338533.3366600. [18] A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwi?ska, S.G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, A.P. Badia, K.M. Hermann, Y. Zwols, G. Ostrovski, A. Cain, H. King, C. Summerfield, P. Blunsom, K. Kavukcuoglu, D. Hassabis, Hybrid computing using a neural network with dynamic external memory, Nature. 538 (2016) 471–476. https://doi.org/10.1038/nature20101. [19] G. Farnadi, J. Tang, M. De Cock, M.-F. Moens, User profiling through deep multimodal fusion, in: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 2018: pp. 171–179. https://doi.org/10.1145/3159652.3159691. [20] A. Farseev, K. Lepikhin, H. Schwartz, E.K. Ang, K. Powar, SoMin.ai: Social multimedia influencer discovery marketplace, in: Proceedings of the 26th ACM International Conference on Multimedia, 2018: pp. 1234–1236. https://doi.org/10.1145/3240508.3241387. [21] S. Wang, T. Gan, Y. Liu, L. Zhang, J. Wu, L. Nie, Discover micro-influencers for brands via better understanding, IEEE Transactions on Multimedia. 24 (2021) 2595–2605. https://doi.org/10.1109/TMM.2021.3087038. [22] S. Wang, T. Gan, Y. Liu, J. Wu, Y. Cheng, L. Nie, Micro-influencer recommendation by multi-perspective account representation learning, DOI: 10.1109/TMM.2022.3151029. (2022). https://doi.org/10.1109/TMM.2022.3151029. [23] X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, M. Wang, LightGCN: Simplifying and powering graph convolution network for recommendation, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020: pp. 639–648. https://doi.org/10.1145/3397271.3401063. [24] H.-C. Lin, P.F. Bruning, H. Swarna, Using online opinion leaders to promote the hedonic and utilitarian value of products and services, Business Horizons. 61 (2018) 431–442. https://doi.org/10.1016/j.bushor.2018.01.010. [25] P. Breves, N. Liebers, M. Abt, A. Kunze, The perceived fit between Instagram influencers and the endorsed brand: How influencer–brand fit affects source credibility and persuasive effectiveness, Journal of Advertising Research. 59 (2019) 440–454. https://doi.org/10.2501/JAR-2019-030. [26] D.Y. Kim, H.-Y. Kim, Influencer advertising on social media: The multiple inference model on influencer-product congruence and sponsorship disclosure, Journal of Business Research. 130 (2021) 405–415. https://doi.org/10.1016/j.jbusres.2020.02.020. [27] G. Ye, L. Hudders, S. De Jans, M. De Veirman, The value of influencer marketing for business: A bibliometric analysis and managerial implications, Journal of Advertising. 50 (2021) 160–178. https://doi.org/10.1080/00913367.2020.1857888. [28] C. Lou, S. Yuan, Influencer marketing: How message value and credibility affect consumer trust of branded content on social media, Journal of Interactive Advertising. 19 (2019) 58–73. https://doi.org/10.1080/15252019.2018.1533501. [29] I.R. Hallac, B. Ay, G. Aydin, User representation learning for social networks: An empirical study, Applied Sciences. 11 (2021) 5489. https://doi.org/10.3390/app11125489. [30] S. Kim, J.-Y. Jiang, M. Nakada, J. Han, W. Wang, Multimodal post attentive profiling for influencer marketing, in: Proceedings of The Web Conference, 2020: pp. 2878–2884. https://doi.org/10.1145/3366423.3380052. [31] J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, in: Proceedings of Neural Information Processing Systems Workshop on Deep Learning, 2014. [32] Y. Kim, Convolutional neural networks for sentence classification, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2014: pp. 1746–1751. https://doi.org/10.3115/v1/D14-1181. [33] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: pp. 770–778. https://doi.org/10.1109/CVPR.2016.90. [34] S. Rendle, C. Freudenthaler, Z. Gantner, L. Schmidt-Thieme, BPR: Bayesian personalized ranking from implicit feedback, in: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009: pp. 452–461. [35] K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in: ArXiv:1409.1556, 2015. [36] T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality, in: Proceedings of the International Conference on Neural Information Processing Systems, 2013: pp. 3111–3119. [37] T. Xiao, Y. Liu, B. Zhou, Y. Jiang, J. Sun, Unified perceptual parsing for scene understanding, in: Proceedings of the European Conference on Computer Vision, 2018: pp. 418–434. [38] B. David, N. Andrew, J. Michael I, Latent Dirichlet allocation, Journal of Machine Learning Research. 3 (2003) 993–1022. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83882 | - |
dc.description.abstract | 隨著網紅行銷越來越受到歡迎,許多品牌雇用網紅,在社群平台上宣傳其產品和品牌形象,在社群平台上,品牌要找合適的網紅,且網紅也要找合適的品牌,但要找合適的合作對象,對雙方都是一個極大的挑戰。因此,提供雙向推薦模型為品牌推薦合適的網紅以及為網紅推薦合適的品牌是迫切且不可或缺的議題。許多研究顯示,Graves等人所提出的記憶體機制可有效學習複雜資料結構的表示式,但目前並未有研究利用記憶體機制從使用者產生內容中學習網紅和品牌的表示式。因此,基於記憶體機制,我們提出一個雙向推薦模型。實驗結果顯示,我們所提出的研究架構優於最先進的網紅推薦模型。我們的研究架構可幫助品牌接觸更多潛在的網紅來推廣他們的產品,也有利於網紅尋找合適的品牌,進而幫助品牌降低口碑行銷成本,亦可幫助網紅節省媒合費用。 | zh_TW |
dc.description.abstract | Influencer marketing has become increasingly popular for brands to promote their products and brand images, and expand their reach on social media platforms. However, it is challenging for brands to find suitable influencers to work with, and for influencers to reach out proper brands to collaborate with. Therefore, it is desirable and essential to offer a two-way recommendation model for brands and influencers. It has been shown that the memory mechanism proposed by Graves et al. can effectively learn the representations of complex data structures. However, to the best of our knowledge, there is no study dedicated to applying the memory mechanism to learn the influencer and brand representations from user-generated contents. Therefore, in this study, based on the memory mechanism, we propose a two-way recommendation model to not only find suitable influencers for brands to promote their products or brand images, but also seek proper brands for influencers. The experiment results show that the proposed framework outperforms the state-of-the-art methods in terms of AUC, F1-score, MRR, mAP and MedR. Our recommender framework is beneficial for not only brands to reach potential influencers to promote their products but also influencers to seek prospective brands for their collaborative work, which in turn help brands implement word-of-mouth marketing strategies, and help influencers save money from the agency service charge. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T21:21:58Z (GMT). No. of bitstreams: 1 U0001-1607202209411900.pdf: 2110478 bytes, checksum: ecfd707b5c9b93c6d57260c5499edab1 (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | Table of Contents i List of Figures ii List of Tables iii Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Influencer Marketing 5 2.2 Influencer Recommendation 6 Chapter 3 The Proposed Framework 8 3.1 Text Encoder 9 3.1.1 Writing 12 3.1.2 Reading 12 3.2 Image Encoder 13 3.3 Interaction Encoder 14 3.4 Two-Way Recommendation Model 15 Chapter 4 Experimental Results 17 4.1 Dataset and Experiment Setup 17 4.2 Performance Evaluation 19 4.3 Effect of Each Encoder 21 4.4 Effect of the Bi-Directional Ranking Mechanism 25 4.5 Effect of the Memory Mechanism 28 Chapter 5 Conclusions and Future Work 38 References 41 | |
dc.language.iso | en | |
dc.title | 社群平台網紅與品牌雙向推薦模型 | zh_TW |
dc.title | A Two-Way Recommendation Model for Influencers and Brands on Social Media Platforms | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.advisor-orcid | 李瑞庭(0000-0003-0320-7309) | |
dc.contributor.oralexamcommittee | 劉敦仁(Duen-Ren Liu),許秉瑜(Ping-Yu Hsu) | |
dc.subject.keyword | 網紅行銷,雙向推薦,社群平台,深度學習,記憶體機制, | zh_TW |
dc.subject.keyword | Influencer marketing,two-way recommendation,social media platform,deep learning,memory mechanism, | en |
dc.relation.page | 44 | |
dc.identifier.doi | 10.6342/NTU202201493 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2022-07-19 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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