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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊裕澤 | zh_TW |
| dc.contributor.advisor | Yuh-Jzer Joung | en |
| dc.contributor.author | 賴彥良 | zh_TW |
| dc.contributor.author | Yen-Liang Lai | en |
| dc.date.accessioned | 2024-08-15T17:09:16Z | - |
| dc.date.available | 2024-08-16 | - |
| dc.date.copyright | 2024-08-15 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-06 | - |
| dc.identifier.citation | Bachman, P., Hjelm, R. D., & Buchwalter, W. (2019). Learning representations by maximizing mutual information across views. Advances in neural information processing systems, 32.
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Advances in Neural Information Processing Systems, 36. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94376 | - |
| dc.description.abstract | 社群貼文的熱門程度往往反映受眾對於內容的喜愛程度，社群網紅或廠商可透過觀察貼文的按讚數變化，制定更有效的行銷策略，進而提升社群行銷的成效。因此，如何準確預測社群貼文的熱門度是一大關鍵。然而，現實世界的社群媒體資料具備不平衡的特性，極冷門與極熱門的貼文往往只具備很少的資料量，造成預測熱門度時的失準。有鑒於近年來對比學習在特徵學習的成功，以及將對比學習概念引進回歸任務的新興趨勢，本研究提出加權排序對比迴歸(Weighted-Rank Contrastive Regression) 損失函數，以解決真實世界的回歸問題中數據不平衡的問題。我們在社群媒體熱門度預測資料集 (B. Wu et al., 2019) 上進行實驗，實驗結果顯示我們的方法優於傳統方法 (僅以 L1 損失函數進行擬合) 和當前的最先進的對比迴歸方法 Rank-N-Contrast (Zha et al., 2024)，尤其在處理高熱門貼文、缺乏負樣本數的異常值方面表現更佳。本研究所提出的加權排序對比迴歸損失函數不僅解決了社群媒體熱門度預測中的數據不平衡問題，更提供了一種可泛化的特徵學習方法，可推廣至其他任意的不平衡迴歸任務中。 | zh_TW |
| dc.description.abstract | Social Media Popularity Prediction (SMPP) is the task of forecasting the level of engagement a social media post will receive. In SMPP, it is crucial for understanding audience engagement and enabling targeted marketing strategies. The popularity of social media posts often reflects the audience’s preference for the content. Social media influencers or brands can design more effective marketing strategies by observing the changes in the number of likes on posts, thereby enhancing the effectiveness of social media marketing. However, the inherent imbalance in real-world social media data, where certain popularity levels are underrepresented, posed a significant challenge. In this study, we leveraged the recent success of contrastive learning and its growing integration into regression tasks, and introduced a weighted-rank contrastive regression loss to counteract the data imbalance challenges. Experiments on the Social Media Prediction Dataset (B. Wu et al., 2019) demonstrated that our method outperformed the vanilla approach (solely fit on L1 loss) and the current state of the art (SOTA) contrastive regression approach Rank-N-Contrast (Zha et al., 2024) , especially for challenging outliers with high popularity and few negative counterparts. The proposed weighted-rank contrastive regression loss not only addressed the inherent data imbalance in SMPP but also offered a robust representation learning solution that could be generalized to other real-world imbalanced regression tasks. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-15T17:09:16Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-15T17:09:16Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
摘要 ii Abstract iii Contents v List of Figures vii List of Tables ix Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Motivation and Objectives 4 Chapter 2 Literature Review 5 2.1 Social Media Popularity Prediction 6 2.1.1 Manually Preprocessed Features 6 2.1.2 Pretrained Models 7 2.2 Overcome the Imbalance Regression 8 2.2.1 Data Imbalance 10 2.2.2 Deep Imbalance Regression 12 2.2.3 Contrastive Regression 18 2.3 Summary 23 Chapter 3 Methodology 24 3.1 Problem Definition 24 3.2 Overview of Our Proposed Framework 24 3.3 Post Representation Extraction 25 3.4 Dense Features 26 3.5 Weighted-Rank Contrastive Regression 26 Chapter 4 Empirical Experiments 30 4.1 Dataset 30 4.2 Evaluation Metrics 30 4.3 Hyperparameters and Experimental Settings 31 4.3.1 Hyperparameters 31 4.3.2 Experimental Settings 32 4.4 Evaluation Results 32 4.5 Additional Experiments 34 4.5.1 Curated Dataset 34 Chapter 5 Conclusion 37 5.1 Conclusion 37 5.2 Limitations and Future Works 38 References 40 Appendix A — Qualitative Visualization 46 A.1 Feature Similarity 46 A.2 Feature Visualization 47 | - |
| dc.language.iso | en | - |
| dc.title | 加權排序對比回歸學習用於資料不平衡之社群媒體熱門度預測 | zh_TW |
| dc.title | Weighted-Rank Contrastive Regression for Robust Learning on Imbalance Social Media Popularity Prediction | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳建錦;楊立偉;陳以錚 | zh_TW |
| dc.contributor.oralexamcommittee | Chien-Chin Chen;Li-Wei Yang;Yi-Cheng Chen | en |
| dc.subject.keyword | 社群媒體,熱門度預測,對比學習,不平衡回歸,網紅行銷, | zh_TW |
| dc.subject.keyword | Social Media,Popularity Prediction,Contrastive Learning,Imbalance Regression,Influencer Marketing, | en |
| dc.relation.page | 48 | - |
| dc.identifier.doi | 10.6342/NTU202403142 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-09 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 資訊管理學系 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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