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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦(Chien Chin Chen) | |
| dc.contributor.author | Ze-Han Fang | en |
| dc.contributor.author | 方澤翰 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:15:55Z | - |
| dc.date.copyright | 2022-09-26 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-09-21 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/84567 | - |
| dc.description.abstract | 主題事件趨勢長久以來都是各國政府及企業所關心的議題，透過主題事件趨勢預測，可以分析一個國家目前的社會經濟狀況及未來國家發展的走向，進而制定適當的決策。隨著資訊科技快速的演進，搜尋引擎已成為群眾在網路上蒐集資訊的重要工具，使用者於搜尋引擎上的行為模式與當前主題事件的發展息息相關，存在於搜尋引擎中的群眾智慧對於主題事件趨勢預測也深具研究發展的潛力。 在本研究中，我們基於隱含於搜尋引擎中的群眾智慧提出了協同式主題事件預測架構以評估主題事件當前的發展狀態。此架構包含兩個主要的貢獻，首先，我們提出了基於群眾智慧的特徵選取方法以找出具有代表性的特徵詞彙當作預測主題事件發展的關鍵指標。接著，我們進一步的基於搜尋引擎中的群眾智慧設計了一套新式的主題事件預測方法，以評估當前主題事件的發展狀態。實驗結果顯示，我們所提出的協同式主題事件預測架構能夠精準的進行預測，並且證明搜尋引擎中的群眾智慧能夠有效的運用於主題事件趨勢預測領域。 | zh_TW |
| dc.description.abstract | In response to rapidly changing situations at the national and international levels, it is important for decision makers to monitor the development of trending topics which are associated with long-running events that affect people’s lives and activities. In recent years, web search engines have become a major platform for the general public to access information. Because the search patterns of search engine users are often correlated with emerging events, the crowdsourcing of search engines has the potential for trend surveillance. In this dissertation, we provide a collaborative trend surveillance framework to estimate the status of trending topics by crowdsourcing the collective wisdom in web search engines. First, we propose a crowdsourced-wisdom-based feature selection method to select representative indicators showing trending topics and concerns of the general public. Then, we describe the design of our novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines. The experiment results show that the collaborative trend surveillance framework performs well and the crowdsourcing of web search engines are helpful for trend surveillance. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:15:55Z (GMT). No. of bitstreams: 1 U0001-2009202220393500.pdf: 2686238 bytes, checksum: 152aa050dcfbc563c19626c4db3cb677 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 謝辭 i 中文摘要 ii 英文摘要 iii Chapter 1 Introduction 1 Chapter 2 Literature Review 5 2.1 Trend Surveillance Systems Using Web Search Engines 5 2.2 Benefits of Crowdsourcing for Web Search Engines 6 2.3 Recommendation Systems and Collaborative Filtering 8 2.4 Serfling Method 10 Chapter 3 Feature Selection Using Crowdsourcing of Web Search Engines 12 3.1 Problem Definition 12 3.2 The Proposed Feature Selection Method 14 3.3 Prediction Model Construct 19 3.4 Performance Evaluation 21 3.4.1 Data Description and Evaluation Metrics 21 3.4.2 Prediction Performance on UIC 25 3.4.3 Prediction Performance on ILI 28 3.4.4 Prediction Performance on NCI 31 3.4.5 Prediction Performance on XBOX 33 3.4.6 Experiment Based on the Sliding Window Approach 35 3.4.7 Experiment Discussion 46 3.5 Conclusion 50 Chapter 4 Trend Prediction Using Crowdsourcing of Web Search Engines 51 4.1 Problem Definition 51 4.2 Feature Selection 52 4.3 The Proposed Prediction Method 56 4.4 Performance Evaluation 60 4.4.1 Dataset description and evaluation metrics 60 4.4.2 Prediction performance on UIC 65 4.4.3 Prediction performance on ILI 68 4.4.4 Prediction performance on NCI 70 4.4.5 Prediction performance on XBOX 72 4.4.6 Experiment Discussion 74 4.5 Query Term Analysis 78 4.6 Conclusion 80 Chapter 5 Conclusions and Future Works 81 References 82 Appendix A 88 Appendix B 89 | |
| dc.language.iso | en | |
| dc.title | 主題事件趨勢研究 | zh_TW |
| dc.title | A Study of Trending Topic Prediction | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 陳孟彰(Meng Chang Chen),張嘉惠(Chia-Hui Chang),盧信銘(Hsin-Min Lu),孔令傑(Ling-Chieh Kung) | |
| dc.subject.keyword | 主題事件趨勢預測,排序學習,特徵選取,機器學習,群眾智慧, | zh_TW |
| dc.subject.keyword | Topic Trend Surveillance,Learning to Rank,Feature Selection,Machine Learning,Crowdsourcing, | en |
| dc.relation.page | 90 | |
| dc.identifier.doi | 10.6342/NTU202203676 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-09-22 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-09-26 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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