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題名 智慧型學術文章推播服務代理人對促進學術傳播之影響研究
The Effects of an Intelligence Academic Article Push-Service Agent on Scholarly Communication作者 簡丞擔
Chien, Cheng-Tan貢獻者 陳志銘
Chen, Chih-Ming
簡丞擔
Chien,Cheng-Tan關鍵詞 學術傳播
學術社群
自動化推薦系統
推播精準度
網路廣告效果
Scholarly communication
Academic community
Automatic push system,
Recommend accuracy
Online advertising effectiveness日期 2020 上傳時間 3-Aug-2020 17:50:38 (UTC+8) 摘要 學術傳播對於學術成果的推廣一直以來都扮演著重要角色，將學術論文、資料分享給更多從事相關研究的學者或是研究人員，有助於讓他們獲得更多的資訊偶遇(Information Encountering)機會。其中，社群網路是一個有效的資訊散播媒介，透過社群網站進行學術傳播可以將具有相同研究興趣的人聚合起來，並組建成學術社群，並在社群內互相傳遞學術相關資訊、針對特定議題進行討論，以及互相交流研究結果。由於目前在中文環境的學術社群較少，因此本研究希望能透過創建學術社群來拉近中文環境中具有相同學術研究領域之學者與研究人員，並透過本研究設計之「智慧型學術文章推播服務代理人(Itelligence Academic Article Push-Service Agent, IAAPSA)」來促進學術論文的傳播，透過擷取社群中的留言資料，並與學術論文資料庫之論文進行比對，將最相關的論文推播至討論社群中，希望藉此增加學術論文與研究人員之資訊偶遇機會，進而提升學術傳播效益。本研究採用單組前實驗研究法，以在臉書建立「圖資與檔案學刊」學術社群，並邀請圖書資訊與檔案學相關研究學者及研究人員加入社群為研究對象，透過IAAPSA將社群內的留言資料與「政大學術集成平台」之論文資料庫進行比對，並將相關論文推播至此一社群中，並記錄被推播論文之下載量來計算下載論文量是否因推薦而有顯著提升。此外，也透過問卷了解所推播之論文是否切合社群的需要，以及貼文對於社群之網路廣告效果，據此評估IAAPSA對於學術文章之推播精準度以及推薦貼文之網路廣告效果量，並綜合探討論文推薦之促進學術傳播效果。研究結果發現，學術論文經過IAAPSA推薦並推播至學術社群之後，其下載量有顯著提升，並且有良好之推播精準度。此外，在貼文之網路廣告效果量上也有良好之效益，顯示IAAPSA能夠將符合社群討論之論文推播至學術社群之中，並且社群成員也會願意點擊並下載全文資料進行閱讀。此外，從訪談質性資料來看，社群成員也肯定IAAPSA所推播之論文符合他們的需求，並認為適合以臉書作為學術社群交流與傳播平台。最後基於本研究之研究結果，提出IAAPSA發布貼文策略之改善建議，以及未來可以繼續進一步探索的研究方向。整體而言，本研究以內容為基礎之推薦(Content-based Recommendation)方法所發展之IAAPSA，可提升學術社群之經營成效，對於促進學術論文之傳播具有創新與貢獻。
Scholarly communication has played a primary role in the promotion of academic outcome, by sharing academic theses and data with more scholars or researchers for more information encountering opportunities. Social network is an effective information dissemination medium. With scholarly communication through social network sites, people with same research interests are gathered and construct the academic community to mutually deliver academic information, discuss specific issues, and exchange research results. There are comparatively few academic communities with Chinese environment. This study therefore expects to bring scholars and researchers with the same academic research fields closer in the Chinese environment through the establishment of academic community. Moreover, the “Intelligent Academic Article Push-Service Agent (IAAPSA)” designed in this study is used for enhancing the communication of academic theses. By capturing messages in the social community and comparing with theses in academic thesis database, the most relevant theses are pushed to the social network. It is expected to enhance the information encountering opportunity of academic theses and researchers to further promote scholarly communication benefit.With one-shot pre-experimental design, the academic community, “Journal of InfoLib & Archives”, is established on Facebook and library, information and archival studies related scholars and researchers are invited to join in the community as the research objects. Through IAAPSA, messages left in the community are compared with the thesis database in “NCCU Academic Hub” and pushed to the community. The downloads of the pushed theses are recorded for calculating whether the downloads significantly increase with recommendations. What is more, the questionnaire is used for understanding whether the pushed theses meet the needs of the community and the online advertising effectiveness of such posts to the community. It aims to evaluate the academic article recommend accuracy of with IAAPSA and the online advertising effectiveness of recommended posts. The effect of thesis recommendation on the enhancement of scholarly communication is comprehensively discussed.The research findings show that the downloads of academic theses pushed to the academic community through the recommendation of IAAPSA are remarkably enhanced and the recommend accuracy is favorable. Besides, the online advertising effectiveness of posts also present good benefits, revealing that IAAPSA could push theses meeting the community discussion to the academic community, and the community members are willing to click and download full-text data for reading. From the qualitative interview data, the community members affirm that the theses pushed by IAAPSA meet their needs and using Facebook as the academic community exchange and community platform is suitable.Based on the research result, improvement for posts with IAAPSA and future research direction are suggested. Overall speaking, IAAPSA developed with Content-based Recommendation in this study could promote the management effect of academic community and present innovation and contribution to enhance the communication of academic theses.參考文獻 曾曉彤（2018）。臉書社群廣告效果研究: Chatbot 與貼文廣告效果之比較。政治大學傳播學院傳播碩士學位學程學位論文 ，1–133。蘇建州（2010）。網路使用者之媒體共用偏好與網路關鍵字廣告效果研究。新聞學研究，103，1–42。吳冠成（2012）。臉書社群網站廣告效果之實證研究。大同大學事業經營學系所學位論文，1–141。戴軒廷、馬恆、張紹勳 (2004)。 影響網路廣告效果之相關因素。中華管理評論國際學報。Achakulvisut, T., Acuna, D. E., Ruangrong, T., & Kording, K. (2016). Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications. PLOS ONE, 11(7), e0158423. https://doi.org/10.1371/journal.pone.0158423Al-Rawi, Ahmed. “News Values on Social Media: News Organizations’ Facebook Use.” Journalism 18, no. 7 (2017): 871–889.Ainin, S., Naqshbandi, M. M., Moghavvemi, S., & Jaafar, N. I. (2015). Facebook usage, socialization and academic performance. Computers & Education, 83, 64–73.Asabere, N. Y., Xia, F., Meng, Q., Li, F., & Liu, H. (2015). Scholarly paper recommendation based on social awareness and folksonomy. International Journal of Parallel, Emergent and Distributed Systems, 30(3), 211–232.Batmaz, Z., Yurekli, A., Bilge, A., & Kaleli, C. (2019). A review on deep learning for recommender systems: Challenges and remedies. Artificial Intelligence Review, 52(1), 1–37. https://doi.org/10.1007/s10462-018-9654-yBeel, J., & Dinesh, S. (2017). Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them. BIR@ ECIR, 6–17.Beel, J., & Langer, S. (2015). A comparison of offline evaluations, online evaluations, and user studies in the context of research-paper recommender systems. International conference on theory and practice of digital libraries, 153–168. Springer.Booker, L., & Bandyopadhyay, S. (2013). How academic libraries can leverage social networking to popularize their services: An empirical study. Journal of the Indiana Academy of the Social Sciences, 16(2), 12.Callaghan, G., & Fribbance, I. (2016). The use of Facebook to build a community for distance learning students: A case study from the Open University. Open Learning: The Journal of Open, Distance and e-Learning, 31(3), 260–272.Chu, Hui-Chun, Gwo-Jen Hwang, Chin-Chung Tsai, and Nian-Shing Chen. “An Innovative Approach for Promoting Information Exchanges and Sharing in a Web 2.0-Based Learning Environment.” Interactive Learning Environments 17, no. 4 (December 2009): 311–23. https://doi.org/10.1080/10494820903195173.Dantonio, L., Makri, S., & Blandford, A. (2012). Coming across academic social media content serendipitously. Proceedings of the American Society for Information Science and Technology, 49(1), 1–10.David, O. N., Helou, A. M., & Rahim, N. Z. A. (2012). Model of perceived influence of academic performance using social networking. International Journal of Computers & Technology, 2(2a), 24–29.De Gemmis, M., Lops, P., Musto, C., Narducci, F., & Semeraro, G. (2015). Semantics-aware content-based recommender systems. 收入 Recommender Systems Handbook (页 119–159). Springer.Erdelez, S., Beheshti, J., Heinström, J., Toms, E., Makri, S., Agarwal, N. K., & Björneborn, L. (2016). Research perspectives on serendipity and information encountering. Proceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology, 11. American Society for Information Science.Han, J., & Yamana, H. (2017). A survey on recommendation methods beyond accuracy. IEICE TRANSACTIONS on Information and Systems, 100(12), 2931–2944.Jordan, K. (2016). Digital scholarship and the social networking site: How academics conceptualise their networks on academic social networking sites and Twitter.Khoo, C. S. (2014). Issues in information behaviour on social media. LIBRES: Library and Information Science Research Electronic Journal, 24(2), 75.Kim, S.-W., & Gil, J.-M. (2019). Research paper classification systems based on TF-IDF and LDA schemes. Human-Centric Computing and Information Sciences, 9(1), 30. https://doi.org/10.1186/s13673-019-0192-7Kirkup, G. (2010). Academic blogging: Academic practice and academic identity. London Review of Education, 8(1), 75–84.Knight, C. G., & Kaye, L. K. (2016). ‘To tweet or not to tweet?’A comparison of academics’ and students’ usage of Twitter in academic contexts. Innovations in education and teaching international, 53(2), 145–155.Knight, G. (2015). Building a research data management service for the London school of hygiene & tropical medicine. Program-Electronic Library and Information Systems, 49(4), 424–439. https://doi.org/10.1108/PROG-01-2015-0011Lee, J., Lee, K., & Kim, J. G. (2013). Personalized academic research paper recommendation system. arXiv preprint arXiv:1304.5457.Lee, M. K., Yoon, H. Y., Smith, M., Park, H. J., & Park, H. W. (2017). Mapping a Twitter scholarly communication network: A case of the association of internet researchers’ conference. Scientometrics, 112(2), 767–797.Lee, S., Ha, T., Lee, D., & Kim, J. H. (2018). Understanding the majority opinion formation process in online environments: An exploratory approach to Facebook. Information Processing & Management, 54(6), 1115–1128. https://doi.org/10.1016/j.ipm.2018.08.002Lin, Chin-Feng, and Chen-Su Fu. “Evaluating Online Advertising Effect: An Approach Integrating Means–End Conceptualization and Similarity Analysis.” Electronic Commerce Research and Applications 32 (2018): 1–12.Liu, Yang, Chen Yang, Jian Ma, Wei Xu, and Zhongsheng Hua. “A Social Recommendation System for Academic Collaboration in Undergraduate Research.” Expert Systems 36, no. 2 (2019): e12365.Lops, P., de Gemmis, M., & Semeraro, G. (2011). Content-based Recommender Systems: State of the Art and Trends. 收入 F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (编), Recommender Systems Handbook (页 73–105). https://doi.org/10.1007/978-0-387-85820-3_3Lou, L., & Koh, J. (2017). Enhancing Fan Participation in Social Media Based Virtual Brand Communities: The Case of Like, Comment, and Share Activities. Asia Pacific Journal of Information Systems, 27(1), 54–76.Menon, S., & Soman, D. (2002). Managing the power of curiosity for effective web advertising strategies. Journal of Advertising, 31(3), 1–14.Ortega, J. L. (2016). To be or not to be on Twitter, and its relationship with the tweeting and citation of research papers. Scientometrics, 109(2), 1353–1364.Ovadia, S. (2013). When social media meets scholarly publishing. Behavioral & Social Sciences Librarian, 32(3), 194–198.Ovadia, S. (2014). ResearchGate and Academia. edu: Academic social networks. Behavioral & social sciences librarian, 33(3), 165–169.Panahi, S., Watson, J., & Partridge, H. (2016). Information encountering on social media and tacit knowledge sharing. Journal of Information Science, 42(4), 539–550.Pontis, S., Kefalidou, G., Blandford, A., Forth, J., Makri, S., Sharples, S., … Woods, M. (2016). Academics’ responses to encountered information: Context matters. Journal of the association for information science and technology, 67(8), 1883–1903.Pursel, B., Liang, C., Wang, S., Wu, Z., Williams, K., Brautigam, B., … Giles, C. L. (2016). BBookX: Design of an Automated Web-based Recommender System for the Creation of Open Learning Content. Proceedings of the 25th International Conference Companion on World Wide Web, 929–933. International World Wide Web Conferences Steering Committee.Roudposhti, V. M., Nilashi, M., Mardani, A., Streimikiene, D., Samad, S., & Ibrahim, O. (2018). A new model for customer purchase intention in e-commerce recommendation agents. Journal of International Studies Vol, 11(4).Suciu, G., Boscher, C., Prioux, L., Pasat, A., & Dobre, C. (2017). Insights into Collaborative Platforms for Social Media Use Cases. Studies in Informatics and Control, 26(4), 435–440.Toreini, P., Chatti, M. A., Thüs, H., & Schroeder, U. (2016). Interest-based recommendation in academic networks using social network analysis. DeLFI 2016–Die 14. E-Learning Fachtagung Informatik.Wu, S.-I., Wei, P.-L., & Chen, J.-H. (2008). Influential factors and relational structure of Internet banner advertising in the tourism industry. Tourism Management, 29(2), 221–236.Xu, Heng, Lih-Bin Oh, and Hock-Hai Teo. “Perceived Effectiveness of Text vs. Multimedia Location-Based Advertising Messaging.” International Journal of Mobile Communications 7, no. 2 (2009): 154–177.Zakrevskaya, N. G., Utisheva, E. V., Bordovskiy, P. G., & Komeva, E. Y. (2018). Electronic information and education environment as academic communication tool. Theory and Practice of Physical Culture, (2), 3–3. 描述 碩士
國立政治大學
圖書資訊與檔案學研究所
107155017資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107155017 資料類型 thesis dc.contributor.advisor 陳志銘 zh_TW dc.contributor.advisor Chen, Chih-Ming en_US dc.contributor.author (Authors) 簡丞擔 zh_TW dc.contributor.author (Authors) Chien,Cheng-Tan en_US dc.creator (作者) 簡丞擔 zh_TW dc.creator (作者) Chien, Cheng-Tan en_US dc.date (日期) 2020 en_US dc.date.accessioned 3-Aug-2020 17:50:38 (UTC+8) - dc.date.available 3-Aug-2020 17:50:38 (UTC+8) - dc.date.issued (上傳時間) 3-Aug-2020 17:50:38 (UTC+8) - dc.identifier (Other Identifiers) G0107155017 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131072 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 圖書資訊與檔案學研究所 zh_TW dc.description (描述) 107155017 zh_TW dc.description.abstract (摘要) 學術傳播對於學術成果的推廣一直以來都扮演著重要角色，將學術論文、資料分享給更多從事相關研究的學者或是研究人員，有助於讓他們獲得更多的資訊偶遇(Information Encountering)機會。其中，社群網路是一個有效的資訊散播媒介，透過社群網站進行學術傳播可以將具有相同研究興趣的人聚合起來，並組建成學術社群，並在社群內互相傳遞學術相關資訊、針對特定議題進行討論，以及互相交流研究結果。由於目前在中文環境的學術社群較少，因此本研究希望能透過創建學術社群來拉近中文環境中具有相同學術研究領域之學者與研究人員，並透過本研究設計之「智慧型學術文章推播服務代理人(Itelligence Academic Article Push-Service Agent, IAAPSA)」來促進學術論文的傳播，透過擷取社群中的留言資料，並與學術論文資料庫之論文進行比對，將最相關的論文推播至討論社群中，希望藉此增加學術論文與研究人員之資訊偶遇機會，進而提升學術傳播效益。本研究採用單組前實驗研究法，以在臉書建立「圖資與檔案學刊」學術社群，並邀請圖書資訊與檔案學相關研究學者及研究人員加入社群為研究對象，透過IAAPSA將社群內的留言資料與「政大學術集成平台」之論文資料庫進行比對，並將相關論文推播至此一社群中，並記錄被推播論文之下載量來計算下載論文量是否因推薦而有顯著提升。此外，也透過問卷了解所推播之論文是否切合社群的需要，以及貼文對於社群之網路廣告效果，據此評估IAAPSA對於學術文章之推播精準度以及推薦貼文之網路廣告效果量，並綜合探討論文推薦之促進學術傳播效果。研究結果發現，學術論文經過IAAPSA推薦並推播至學術社群之後，其下載量有顯著提升，並且有良好之推播精準度。此外，在貼文之網路廣告效果量上也有良好之效益，顯示IAAPSA能夠將符合社群討論之論文推播至學術社群之中，並且社群成員也會願意點擊並下載全文資料進行閱讀。此外，從訪談質性資料來看，社群成員也肯定IAAPSA所推播之論文符合他們的需求，並認為適合以臉書作為學術社群交流與傳播平台。最後基於本研究之研究結果，提出IAAPSA發布貼文策略之改善建議，以及未來可以繼續進一步探索的研究方向。整體而言，本研究以內容為基礎之推薦(Content-based Recommendation)方法所發展之IAAPSA，可提升學術社群之經營成效，對於促進學術論文之傳播具有創新與貢獻。 zh_TW dc.description.abstract (摘要) Scholarly communication has played a primary role in the promotion of academic outcome, by sharing academic theses and data with more scholars or researchers for more information encountering opportunities. Social network is an effective information dissemination medium. With scholarly communication through social network sites, people with same research interests are gathered and construct the academic community to mutually deliver academic information, discuss specific issues, and exchange research results. There are comparatively few academic communities with Chinese environment. This study therefore expects to bring scholars and researchers with the same academic research fields closer in the Chinese environment through the establishment of academic community. Moreover, the “Intelligent Academic Article Push-Service Agent (IAAPSA)” designed in this study is used for enhancing the communication of academic theses. By capturing messages in the social community and comparing with theses in academic thesis database, the most relevant theses are pushed to the social network. It is expected to enhance the information encountering opportunity of academic theses and researchers to further promote scholarly communication benefit.With one-shot pre-experimental design, the academic community, “Journal of InfoLib & Archives”, is established on Facebook and library, information and archival studies related scholars and researchers are invited to join in the community as the research objects. Through IAAPSA, messages left in the community are compared with the thesis database in “NCCU Academic Hub” and pushed to the community. The downloads of the pushed theses are recorded for calculating whether the downloads significantly increase with recommendations. What is more, the questionnaire is used for understanding whether the pushed theses meet the needs of the community and the online advertising effectiveness of such posts to the community. It aims to evaluate the academic article recommend accuracy of with IAAPSA and the online advertising effectiveness of recommended posts. The effect of thesis recommendation on the enhancement of scholarly communication is comprehensively discussed.The research findings show that the downloads of academic theses pushed to the academic community through the recommendation of IAAPSA are remarkably enhanced and the recommend accuracy is favorable. Besides, the online advertising effectiveness of posts also present good benefits, revealing that IAAPSA could push theses meeting the community discussion to the academic community, and the community members are willing to click and download full-text data for reading. From the qualitative interview data, the community members affirm that the theses pushed by IAAPSA meet their needs and using Facebook as the academic community exchange and community platform is suitable.Based on the research result, improvement for posts with IAAPSA and future research direction are suggested. Overall speaking, IAAPSA developed with Content-based Recommendation in this study could promote the management effect of academic community and present innovation and contribution to enhance the communication of academic theses. en_US dc.description.tableofcontents 目次摘要 II第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 5第三節 研究問題 6第四節 研究範圍與限制 7第五節 名詞解釋 8第二章 文獻探討 10第一節 社群傳播 10第二節 以內容為基礎之推薦 13第三節 判斷學術傳播效果的因素 16第三章 系統設計 18第一節 系統設計理念 18第二節 系統架構介紹 20第三節 系統元件說明 23第四節 系統開發環境 28第四章 研究設計與實施 29第一節 研究架構 29第二節 研究方法 32第三節 研究對象 33第四節 實驗設計與流程 34第五節 研究工具 38第六節 資料處理與分析 41第七節 研究實施步驟 42第五章 實驗結果分析 44第一節 學術論文經IAAPSA推播後之下載成長量分析 45第二節 IAAPSA之推播精準度分析 46第三節 學術論文推播於學術社群之網路廣告效果量分析 47第四節 訪談資料分析 48第五節 綜合討論 58第六章 結論與建議 63第一節 結論 63第二節 IAAPSA之論文推播改善建議與心得 65第三節 未來研究方向 68參考文獻 69附錄一 推播精準度問卷 73附錄二 網路廣告效果量表 74附錄三 訪談大綱 75 zh_TW dc.format.extent 2915209 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107155017 en_US dc.subject (關鍵詞) 學術傳播 zh_TW dc.subject (關鍵詞) 學術社群 zh_TW dc.subject (關鍵詞) 自動化推薦系統 zh_TW dc.subject (關鍵詞) 推播精準度 zh_TW dc.subject (關鍵詞) 網路廣告效果 zh_TW dc.subject (關鍵詞) Scholarly communication en_US dc.subject (關鍵詞) Academic community en_US dc.subject (關鍵詞) Automatic push system, en_US dc.subject (關鍵詞) Recommend accuracy en_US dc.subject (關鍵詞) Online advertising effectiveness en_US dc.title (題名) 智慧型學術文章推播服務代理人對促進學術傳播之影響研究 zh_TW dc.title (題名) The Effects of an Intelligence Academic Article Push-Service Agent on Scholarly Communication en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 曾曉彤（2018）。臉書社群廣告效果研究: Chatbot 與貼文廣告效果之比較。政治大學傳播學院傳播碩士學位學程學位論文 ，1–133。蘇建州（2010）。網路使用者之媒體共用偏好與網路關鍵字廣告效果研究。新聞學研究，103，1–42。吳冠成（2012）。臉書社群網站廣告效果之實證研究。大同大學事業經營學系所學位論文，1–141。戴軒廷、馬恆、張紹勳 (2004)。 影響網路廣告效果之相關因素。中華管理評論國際學報。Achakulvisut, T., Acuna, D. E., Ruangrong, T., & Kording, K. (2016). Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications. PLOS ONE, 11(7), e0158423. https://doi.org/10.1371/journal.pone.0158423Al-Rawi, Ahmed. “News Values on Social Media: News Organizations’ Facebook Use.” Journalism 18, no. 7 (2017): 871–889.Ainin, S., Naqshbandi, M. M., Moghavvemi, S., & Jaafar, N. I. (2015). Facebook usage, socialization and academic performance. Computers & Education, 83, 64–73.Asabere, N. Y., Xia, F., Meng, Q., Li, F., & Liu, H. (2015). Scholarly paper recommendation based on social awareness and folksonomy. International Journal of Parallel, Emergent and Distributed Systems, 30(3), 211–232.Batmaz, Z., Yurekli, A., Bilge, A., & Kaleli, C. (2019). A review on deep learning for recommender systems: Challenges and remedies. Artificial Intelligence Review, 52(1), 1–37. https://doi.org/10.1007/s10462-018-9654-yBeel, J., & Dinesh, S. (2017). Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them. BIR@ ECIR, 6–17.Beel, J., & Langer, S. (2015). A comparison of offline evaluations, online evaluations, and user studies in the context of research-paper recommender systems. International conference on theory and practice of digital libraries, 153–168. Springer.Booker, L., & Bandyopadhyay, S. (2013). How academic libraries can leverage social networking to popularize their services: An empirical study. Journal of the Indiana Academy of the Social Sciences, 16(2), 12.Callaghan, G., & Fribbance, I. (2016). The use of Facebook to build a community for distance learning students: A case study from the Open University. Open Learning: The Journal of Open, Distance and e-Learning, 31(3), 260–272.Chu, Hui-Chun, Gwo-Jen Hwang, Chin-Chung Tsai, and Nian-Shing Chen. “An Innovative Approach for Promoting Information Exchanges and Sharing in a Web 2.0-Based Learning Environment.” Interactive Learning Environments 17, no. 4 (December 2009): 311–23. https://doi.org/10.1080/10494820903195173.Dantonio, L., Makri, S., & Blandford, A. (2012). Coming across academic social media content serendipitously. Proceedings of the American Society for Information Science and Technology, 49(1), 1–10.David, O. N., Helou, A. M., & Rahim, N. Z. A. (2012). Model of perceived influence of academic performance using social networking. International Journal of Computers & Technology, 2(2a), 24–29.De Gemmis, M., Lops, P., Musto, C., Narducci, F., & Semeraro, G. (2015). Semantics-aware content-based recommender systems. 收入 Recommender Systems Handbook (页 119–159). Springer.Erdelez, S., Beheshti, J., Heinström, J., Toms, E., Makri, S., Agarwal, N. K., & Björneborn, L. (2016). Research perspectives on serendipity and information encountering. Proceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology, 11. American Society for Information Science.Han, J., & Yamana, H. (2017). A survey on recommendation methods beyond accuracy. IEICE TRANSACTIONS on Information and Systems, 100(12), 2931–2944.Jordan, K. (2016). Digital scholarship and the social networking site: How academics conceptualise their networks on academic social networking sites and Twitter.Khoo, C. S. (2014). Issues in information behaviour on social media. LIBRES: Library and Information Science Research Electronic Journal, 24(2), 75.Kim, S.-W., & Gil, J.-M. (2019). Research paper classification systems based on TF-IDF and LDA schemes. Human-Centric Computing and Information Sciences, 9(1), 30. https://doi.org/10.1186/s13673-019-0192-7Kirkup, G. (2010). Academic blogging: Academic practice and academic identity. London Review of Education, 8(1), 75–84.Knight, C. G., & Kaye, L. K. (2016). ‘To tweet or not to tweet?’A comparison of academics’ and students’ usage of Twitter in academic contexts. Innovations in education and teaching international, 53(2), 145–155.Knight, G. (2015). Building a research data management service for the London school of hygiene & tropical medicine. Program-Electronic Library and Information Systems, 49(4), 424–439. https://doi.org/10.1108/PROG-01-2015-0011Lee, J., Lee, K., & Kim, J. G. (2013). Personalized academic research paper recommendation system. arXiv preprint arXiv:1304.5457.Lee, M. K., Yoon, H. Y., Smith, M., Park, H. J., & Park, H. W. (2017). Mapping a Twitter scholarly communication network: A case of the association of internet researchers’ conference. Scientometrics, 112(2), 767–797.Lee, S., Ha, T., Lee, D., & Kim, J. H. (2018). Understanding the majority opinion formation process in online environments: An exploratory approach to Facebook. Information Processing & Management, 54(6), 1115–1128. https://doi.org/10.1016/j.ipm.2018.08.002Lin, Chin-Feng, and Chen-Su Fu. “Evaluating Online Advertising Effect: An Approach Integrating Means–End Conceptualization and Similarity Analysis.” Electronic Commerce Research and Applications 32 (2018): 1–12.Liu, Yang, Chen Yang, Jian Ma, Wei Xu, and Zhongsheng Hua. “A Social Recommendation System for Academic Collaboration in Undergraduate Research.” Expert Systems 36, no. 2 (2019): e12365.Lops, P., de Gemmis, M., & Semeraro, G. (2011). Content-based Recommender Systems: State of the Art and Trends. 收入 F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor (编), Recommender Systems Handbook (页 73–105). https://doi.org/10.1007/978-0-387-85820-3_3Lou, L., & Koh, J. (2017). Enhancing Fan Participation in Social Media Based Virtual Brand Communities: The Case of Like, Comment, and Share Activities. Asia Pacific Journal of Information Systems, 27(1), 54–76.Menon, S., & Soman, D. (2002). Managing the power of curiosity for effective web advertising strategies. Journal of Advertising, 31(3), 1–14.Ortega, J. L. (2016). To be or not to be on Twitter, and its relationship with the tweeting and citation of research papers. Scientometrics, 109(2), 1353–1364.Ovadia, S. (2013). When social media meets scholarly publishing. Behavioral & Social Sciences Librarian, 32(3), 194–198.Ovadia, S. (2014). ResearchGate and Academia. edu: Academic social networks. Behavioral & social sciences librarian, 33(3), 165–169.Panahi, S., Watson, J., & Partridge, H. (2016). Information encountering on social media and tacit knowledge sharing. Journal of Information Science, 42(4), 539–550.Pontis, S., Kefalidou, G., Blandford, A., Forth, J., Makri, S., Sharples, S., … Woods, M. (2016). Academics’ responses to encountered information: Context matters. Journal of the association for information science and technology, 67(8), 1883–1903.Pursel, B., Liang, C., Wang, S., Wu, Z., Williams, K., Brautigam, B., … Giles, C. L. (2016). BBookX: Design of an Automated Web-based Recommender System for the Creation of Open Learning Content. Proceedings of the 25th International Conference Companion on World Wide Web, 929–933. International World Wide Web Conferences Steering Committee.Roudposhti, V. M., Nilashi, M., Mardani, A., Streimikiene, D., Samad, S., & Ibrahim, O. (2018). A new model for customer purchase intention in e-commerce recommendation agents. Journal of International Studies Vol, 11(4).Suciu, G., Boscher, C., Prioux, L., Pasat, A., & Dobre, C. (2017). Insights into Collaborative Platforms for Social Media Use Cases. Studies in Informatics and Control, 26(4), 435–440.Toreini, P., Chatti, M. A., Thüs, H., & Schroeder, U. (2016). Interest-based recommendation in academic networks using social network analysis. DeLFI 2016–Die 14. E-Learning Fachtagung Informatik.Wu, S.-I., Wei, P.-L., & Chen, J.-H. (2008). Influential factors and relational structure of Internet banner advertising in the tourism industry. Tourism Management, 29(2), 221–236.Xu, Heng, Lih-Bin Oh, and Hock-Hai Teo. “Perceived Effectiveness of Text vs. Multimedia Location-Based Advertising Messaging.” International Journal of Mobile Communications 7, no. 2 (2009): 154–177.Zakrevskaya, N. G., Utisheva, E. V., Bordovskiy, P. G., & Komeva, E. Y. (2018). Electronic information and education environment as academic communication tool. Theory and Practice of Physical Culture, (2), 3–3. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001168 en_US
