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題名 線上論壇提問推薦機制：以醫療照護問答網站為例
A posting recommendation system for question formulation: A healthcare Q&A forum study作者 陳怡儒
Chen, Yi-Ju貢獻者 林怡伶
Lin, Yi-Ling
陳怡儒
Chen, Yi-Ju關鍵詞 問答論壇
醫療資訊學
推薦系統
主題式推薦系統
詞向量
語意關聯
Word2Vec
WordNet
使用者生成內容
設計科學
使用者研究
廣義估計式
Question-answering forum
Healthcare informatics
Recommendation system
Topic-based recommendation
Word embedding
Semantic relatedness
Word2Vec
WordNet
User-generated content
Design science
User study
Generalized estimating equations日期 2019 上傳時間 5-Sep-2019 15:45:08 (UTC+8) 摘要 搜尋引擎是現代人慣用搜尋解答的工具，但不是所有人皆可以在搜索引擎上透過精準的查詢句或關鍵字快速找到所需的資訊。有別於搜尋引擎，線上問答論壇並未限制使用者如何提出問題才能找到答案，自由式的書寫環境逐漸吸引許多用戶到平台上尋找有用的資訊，並解決他們在特定領域遇到的困難。然而使用者生成內容不可避免會有表達不清的問題，尤其是特定領域的提問。因為提問者通常不具備專業背景，進而讓論壇分類系統無法成功匹配或是提問目的模糊而得不到專業人士的解答。所以我們設計出一個貼文撰寫推薦系統希望能提高線上論壇的可用性。常見的推薦機制傾向於匹配合適的回覆者或向用戶提出已存在的相似問題，但沒有人去研究如何在詢問過程中撰寫優良的貼文。本文將著重於醫療保健案例中，使用新式推薦系統是否能優化提問者的貼文。我們採用設計科學的方法來完成貼文撰寫主題式推薦系統的研究。目標是協助用戶更了解自己的疑問以組織想法來寫出不錯的貼文，最終獲得有效的答案。我們的推薦系統會透過使用者研究的方式，聘請27名參與者，在兩種保健情境下使用三種模型進行廣義估計式分析，了解使用者是否會因為推薦系統而去優化撰寫的貼文。此外，我們招募兩名從事醫療職業相關的專家，請他們對推薦系統修改後的貼文作評分，驗證是否能讓專家迅速解決問題。我們的結果說明，即使主題式推薦系統的可用性仍需要更多證據來證明，貼文撰寫推薦系統的表現確實與不具備推薦系統的貼文撰寫情況不同。其次，兩位專家評分的結果顯示，添加更多病況資訊並使用貼文撰寫主題式推薦系統，的確增加了獲得更高分評價的可能性。雖然此篇論文遇到了一些問題無法直接證實我們目前主題式的設計可行，不過我們確定了協助寫作貼文的推薦機制是值得探討的。對於常需要協助提問者的服務提供方，優化貼文的機制將能協助服務方製作常見問題與解答，並在撰寫過程中引導提問者至解答頁面，儘速解決問題。
People are getting used to google when they confuse about something, but the truth is not everyone can formulate clear queries on a search engine. Different from the search engine, online Q&A forums allow people to write anything and any styles they want. This free-writing environment gradually attracts more users to search for useful information and deal with their difficulties of specific domains on this platform. However, we cannot control the quality of user-generated content. Few askers have professional backgrounds, so unclear posts may be categorized wrong by supportive systems or difficult to be understood by experts. So, we design a posting recommender and expect it can improve the usability of online forums. In the past, recommendation mechanisms matched suitable repliers or suggested similar questions to users, but no one investigated how to write better posts. This thesis focuses on a new recommendation system to support askers optimizing posts under a healthcare study. We apply the design science methodology to develop a topic-based posting recommender. Its goal is to support users in understanding more about self-issues, organizing thoughts to write reasonable posts and then receiving effective answers. Our posting recommender is evaluated by generalized estimating equations with a user study containing 27 participants, three models, and two healthcare conditions to see if users become more engaged in the question generation. In addition, we recruited two medical-related experts to verify whether modified posts attract them to answer quickly by rating posts from participants. Though the usefulness of our posting recommender still needs more evidence to prove, the result indicates that the performance of a posting recommender acts differently from not having the recommendation system. Another result of analysing experts shows adding more information and using a posting recommender enhance the possibility to get higher rating points. The thesis does encounter some problems to confirm the design effectiveness at this stage, but the posting recommender is proved to be worth investigating. Those who need more efforts to deal with askers may think the posting recommender mechanism useful. The mechanism of optimized posts can help service provider arrange frequently asked questions and guide askers to find the correct answer before posting.參考文獻 Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008). Finding high-quality content in social media. Proceedings of the International Conference on Web Search and Web Data Mining - WSDM ’08, 183.Aigner, M., & Ziegler, G. M. (2018). Completing Latin squares. Proofs from THE BOOK, 1–326.Arora, S., Li, Y., Liang, Y., Ma, T., & Risteski, A. (2016). A latent variable model approach to PMI-based word embeddings. Transactions of the Association for Computational Linguistics, 4, 385–399.Baltadzhieva, A. (2015). Question quality in community question answering forums : a survey. Sigkdd, 17(1), 8–13.Beel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., & Nurnberger, A. (2013). Research paper recommender system evaluation : A quantitative literature survey. Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference, 20(April 2013), 15–22.Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12. https://doi.org/10.1023/A:1021240730564Denecke, K., & Nejdl, W. (2009). How valuable is medical social media data? Content analysis of the medical web. Information Sciences, 179(12), 1870–1880.Dror, G., Koren, Y., Maarek, Y., & Szpektor, I. (2010). I want to answer, who has a question? Yahoo! Answers recommender system. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Feng, M., Xiang, B., Glass, M. R., Wang, L., & Zhou, B. (2016). Applying deep learning to answer selection: A study and an open task. In 2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings.Fox, S. (2013). Health Online 2013: 35% of U.S. adults have gone online to figure out a medical condition; of these, half followed up with a visit to a medical professional. PEW INTERNET & AMERICAN LIFE PROJECT, (January).Fox, S., & Fallows, D. (2003). Internet health resources: Health searches and email have become more commonplace , but there is room for improvement in searches and overall Internet access Findings. PEW INTERNET & AMERICAN LIFE PROJECT, (July).Gittens, A., Achlioptas, D., & Mahoney, M. W. (2017). Skip-Gram - Zipf + Uniform = Vector Additivity. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 69–76.Höchstötter, N., & Lewandowski, D. (2006). What users see structures in search engine results pages.Isern, D., & Moreno, A. (2016). A systematic literature review of agents applied in healthcare. J Med Syst., 40(2).Jeon, J., Croft, W. B., & Lee, J. H. (2005). Finding similar questions in large question and answer archives. Proceedings of the 14th ACM International Conference on Information and Knowledge Management - CIKM ’05, 84.John, M. (1996). A new statistical parser based on bigram lexical dependencies. Science, 184–191.Kim, J.-H., Lee, J.-H., Park, J.-S., Lee, Y., & Rim, K. (2009). Design of diet recommendation system for healthcare service based on user information. Convergence Information Technology, International Conference on (Vol. 0).Lai, S., Liu, K., He, S., & Zhao, J. (2016). How to generate a good word embedding. 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Proceedings of the 1st ACM International Health Informatics Symposium, 210–219.Zhao, J., Collins, C., Chevalier, F., & Balakrishnan, R. (2013). Interactive exploration of implicit and explicit relations in faceted datasets. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2080–2089.Zhou, G., He, T., Zhao, J., & Hu, P. (2015). Learning continuous word embedding with metadata for question retrieval in community question answering. Acl, 250–259. 描述 碩士
國立政治大學
資訊管理學系
106356025資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106356025 資料類型 thesis dc.contributor.advisor 林怡伶 zh_TW dc.contributor.advisor Lin, Yi-Ling en_US dc.contributor.author (Authors) 陳怡儒 zh_TW dc.contributor.author (Authors) Chen, Yi-Ju en_US dc.creator (作者) 陳怡儒 zh_TW dc.creator (作者) Chen, Yi-Ju en_US dc.date (日期) 2019 en_US dc.date.accessioned 5-Sep-2019 15:45:08 (UTC+8) - dc.date.available 5-Sep-2019 15:45:08 (UTC+8) - dc.date.issued (上傳時間) 5-Sep-2019 15:45:08 (UTC+8) - dc.identifier (Other Identifiers) G0106356025 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125531 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 106356025 zh_TW dc.description.abstract (摘要) 搜尋引擎是現代人慣用搜尋解答的工具，但不是所有人皆可以在搜索引擎上透過精準的查詢句或關鍵字快速找到所需的資訊。有別於搜尋引擎，線上問答論壇並未限制使用者如何提出問題才能找到答案，自由式的書寫環境逐漸吸引許多用戶到平台上尋找有用的資訊，並解決他們在特定領域遇到的困難。然而使用者生成內容不可避免會有表達不清的問題，尤其是特定領域的提問。因為提問者通常不具備專業背景，進而讓論壇分類系統無法成功匹配或是提問目的模糊而得不到專業人士的解答。所以我們設計出一個貼文撰寫推薦系統希望能提高線上論壇的可用性。常見的推薦機制傾向於匹配合適的回覆者或向用戶提出已存在的相似問題，但沒有人去研究如何在詢問過程中撰寫優良的貼文。本文將著重於醫療保健案例中，使用新式推薦系統是否能優化提問者的貼文。我們採用設計科學的方法來完成貼文撰寫主題式推薦系統的研究。目標是協助用戶更了解自己的疑問以組織想法來寫出不錯的貼文，最終獲得有效的答案。我們的推薦系統會透過使用者研究的方式，聘請27名參與者，在兩種保健情境下使用三種模型進行廣義估計式分析，了解使用者是否會因為推薦系統而去優化撰寫的貼文。此外，我們招募兩名從事醫療職業相關的專家，請他們對推薦系統修改後的貼文作評分，驗證是否能讓專家迅速解決問題。我們的結果說明，即使主題式推薦系統的可用性仍需要更多證據來證明，貼文撰寫推薦系統的表現確實與不具備推薦系統的貼文撰寫情況不同。其次，兩位專家評分的結果顯示，添加更多病況資訊並使用貼文撰寫主題式推薦系統，的確增加了獲得更高分評價的可能性。雖然此篇論文遇到了一些問題無法直接證實我們目前主題式的設計可行，不過我們確定了協助寫作貼文的推薦機制是值得探討的。對於常需要協助提問者的服務提供方，優化貼文的機制將能協助服務方製作常見問題與解答，並在撰寫過程中引導提問者至解答頁面，儘速解決問題。 zh_TW dc.description.abstract (摘要) People are getting used to google when they confuse about something, but the truth is not everyone can formulate clear queries on a search engine. Different from the search engine, online Q&A forums allow people to write anything and any styles they want. This free-writing environment gradually attracts more users to search for useful information and deal with their difficulties of specific domains on this platform. However, we cannot control the quality of user-generated content. Few askers have professional backgrounds, so unclear posts may be categorized wrong by supportive systems or difficult to be understood by experts. So, we design a posting recommender and expect it can improve the usability of online forums. In the past, recommendation mechanisms matched suitable repliers or suggested similar questions to users, but no one investigated how to write better posts. This thesis focuses on a new recommendation system to support askers optimizing posts under a healthcare study. We apply the design science methodology to develop a topic-based posting recommender. Its goal is to support users in understanding more about self-issues, organizing thoughts to write reasonable posts and then receiving effective answers. Our posting recommender is evaluated by generalized estimating equations with a user study containing 27 participants, three models, and two healthcare conditions to see if users become more engaged in the question generation. In addition, we recruited two medical-related experts to verify whether modified posts attract them to answer quickly by rating posts from participants. Though the usefulness of our posting recommender still needs more evidence to prove, the result indicates that the performance of a posting recommender acts differently from not having the recommendation system. Another result of analysing experts shows adding more information and using a posting recommender enhance the possibility to get higher rating points. The thesis does encounter some problems to confirm the design effectiveness at this stage, but the posting recommender is proved to be worth investigating. Those who need more efforts to deal with askers may think the posting recommender mechanism useful. The mechanism of optimized posts can help service provider arrange frequently asked questions and guide askers to find the correct answer before posting. en_US dc.description.tableofcontents CHAPTER 1 INTRODUCTION 61.1 Background and Motivation 61.2 Research Purpose and Questions 81.3 Contribution 101.4 Content organization 11CHAPTER 2 LITERATURE REVIEW 122.1 Recommendation mechanisms in healthcare domain 122.2 Recommendation in the asking process 13CHAPTER 3 RESEARCH METHODOLOGY 153.1 Identify problem & motivation 153.2 Define objectives of a solution 163.3 Design and development 193.3.1 System layout 203.3.2 Data preparation of the RS 223.3.3 Features extraction 233.3.4 Recommender systems’ implementation 243.4 User study 263.4.1 Dataset 263.4.2 Models 263.4.3 Tasks and experimental materials 263.4.4 Participants and procedure 293.5 Evaluation 303.5.1 User study evaluation 303.5.2 Judgements 313.6 Communication 32CHAPTER 4 ANALYSIS AND RESULTS 334.1 Demographic information 334.2 Analysis of results 364.2.1 Effectiveness 364.2.2 Efficiency 404.2.3 Circumstance 424.3 Experts rate posts 444.3.1 Pharmacy expert 444.3.2 Medical expert 454.3.3 Interaction analysis between model and description 464.4 Satisfaction questionnaire 48CHAPTER 5 DISCUSSION 505.1 Effectiveness perspective 505.2 Efficiency perspective 515.3 Circumstance perspective 525.4 Subjective adjudgment 535.5 Discussion of questionnaire 54CHAPTER 6 CONCLUSION 55APPENDIX 1 – Supportive paragraphs for participants 58REFERENCE 64 zh_TW dc.format.extent 2092444 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106356025 en_US dc.subject (關鍵詞) 問答論壇 zh_TW dc.subject (關鍵詞) 醫療資訊學 zh_TW dc.subject (關鍵詞) 推薦系統 zh_TW dc.subject (關鍵詞) 主題式推薦系統 zh_TW dc.subject (關鍵詞) 詞向量 zh_TW dc.subject (關鍵詞) 語意關聯 zh_TW dc.subject (關鍵詞) Word2Vec zh_TW dc.subject (關鍵詞) WordNet zh_TW dc.subject (關鍵詞) 使用者生成內容 zh_TW dc.subject (關鍵詞) 設計科學 zh_TW dc.subject (關鍵詞) 使用者研究 zh_TW dc.subject (關鍵詞) 廣義估計式 zh_TW dc.subject (關鍵詞) Question-answering forum en_US dc.subject (關鍵詞) Healthcare informatics en_US dc.subject (關鍵詞) Recommendation system en_US dc.subject (關鍵詞) Topic-based recommendation en_US dc.subject (關鍵詞) Word embedding en_US dc.subject (關鍵詞) Semantic relatedness en_US dc.subject (關鍵詞) Word2Vec en_US dc.subject (關鍵詞) WordNet en_US dc.subject (關鍵詞) User-generated content en_US dc.subject (關鍵詞) Design science en_US dc.subject (關鍵詞) User study en_US dc.subject (關鍵詞) Generalized estimating equations en_US dc.title (題名) 線上論壇提問推薦機制：以醫療照護問答網站為例 zh_TW dc.title (題名) A posting recommendation system for question formulation: A healthcare Q&A forum study en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Agichtein, E., Castillo, C., Donato, D., Gionis, A., & Mishne, G. (2008). Finding high-quality content in social media. Proceedings of the International Conference on Web Search and Web Data Mining - WSDM ’08, 183.Aigner, M., & Ziegler, G. M. (2018). Completing Latin squares. Proofs from THE BOOK, 1–326.Arora, S., Li, Y., Liang, Y., Ma, T., & Risteski, A. (2016). A latent variable model approach to PMI-based word embeddings. Transactions of the Association for Computational Linguistics, 4, 385–399.Baltadzhieva, A. (2015). Question quality in community question answering forums : a survey. Sigkdd, 17(1), 8–13.Beel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., & Nurnberger, A. (2013). Research paper recommender system evaluation : A quantitative literature survey. Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference, 20(April 2013), 15–22.Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. 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