政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136847
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113451/144438 (79%)
造访人次 : 51267704      在线人数 : 812
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/136847


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/136847


    题名: 以個人化建構微時刻推薦系統的互動機制
    A personalized Interactive Mechanism Framework for Micro-moment Recommender System
    作者: 李紹威
    Lee, Shao-Wei
    贡献者: 林怡伶
    Lin, Yi-Ling
    李紹威
    Lee, Shao-Wei
    关键词: 微時刻推薦系統
    個人化
    動機賦能
    互動機制
    Micro-moment recommender system
    Personalization
    motivational affordance
    Interactive mechanism
    日期: 2021
    上传时间: 2021-09-02 15:54:54 (UTC+8)
    摘要: 微時刻概念的出現凸現了情境對人們造成的影響,而推薦系統應該要順應這樣的趨勢做出改變。為了搜集到足夠的情境資料,微時刻推薦系統必須要有有效的互動機制,讓使用者和系統之間可以方便的互動。本研究採用了支援自治和本體的設計原理,混合不同種類的個人化去設計了四種互動機制,並且將他們實作在一個微時刻推薦應用程式中。本研究的目的是想了解哪一種互動機制最適合微時刻推薦系統的互動機制,根據我們採用的設計原理和微時刻推薦系統的特性,我們認為愈能讓使用者掌控系統和花費較少心力的設計應該會較為適合。我們藉由為期兩週的受測者間實驗去驗證我們的假設。在實驗中我們讓受測者實際使用我們的應用程式,並收集他們的回饋和使用時的紀錄。我們發現在不同的互動機制中存在控制感受的差異,以採用使用者發起和使用者與系統共同發起的個人化的互動機制較高,而且額外的控制不會讓受測者花費多餘的心力。因此我們認為這兩種設計較適合微時刻推薦系統的互動機制。
    The emergence of the micro-moment concept highlights the influence of context, and the recommender system should be adjusted according to this trend. In order to collect enough contextual information, the micro-moment recommender system (MMRS) have an effective interactive mechanism that allows users to easily interact with the system. This study adopts the design principle of supporting autonomy and promoting the creation and expression of self-identity, mixes different types of personalization to design four types of interactive mechanisms, and implements them in a micro-moment recommender app. The purpose of this study is to understand which interactive mechanism is the most suitable for MMRS. Based on the design principles we adopted and the characteristics of MMRS, we believe that the design that allows users to have more control over the system and uses less effort should be more suitable for supporting micro-moment needs. We tested our hypothesis by a two-week between-subject field study. In the field study, the participants use our app and provide their feedback. We found that there is a difference in perceived active control among different interactive mechanisms, with user-initiated personalized intention and mix-initiated personalized intention personalization mechanisms having higher perceived active control, and the additional control does not cost the participants extra effort. Therefore, we believe that these two designs are more suitable for the MMRS interactive mechanism.
    參考文獻: Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., and Steggles, P. 1999. “Towards a Better Understanding of Context and Context-Awareness,” in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
    Adomavicius, G., Mobasher, B., Ricci, F., and Tuzhilin, A. 2011. Context-Aware Recommender Systems.
    Alsalemi, A., Sardianos, C., Bensaali, F., Varlamis, I., Amira, A., and Dimitrakopoulos, G. 2019. “The Role of Micro-Moments: A Survey of Habitual Behavior Change and Recommender Systems for Energy Saving,” IEEE Systems Journal (13:3), IEEE, pp. 3376–3387.
    Baltrunas, L., Ludwig, B., Peer, S., and Ricci, F. 2013. “Context Relevance Assessment and Exploitation in Mobile Recommender Systems,” Personal and Ubiquitous Computing (16:5), pp. 507–526.
    Barkhuus, L., and Dey, A. 2003. “Is Context-Aware Computing Taking Control Away from the User? Three Levels of Interactivity Examined.”
    Baudisch, P., and Terveen, L. 1999. Interacting with Recommender Systems, (MAY), p. 164.
    Bilos, A., Turkalj, D., and Kelic, I. 2018. “Micro-Moments of User Experience: An Approach to Understanding Online User Intentions and Behavior,” Croatian Direct Marketing Association Conference (1:October), pp. 67–77.
    Biloš, A., Turkalj, D., and Kelić, I. 2018. “Micro-Moments of User Experience: An Approach To Understanding Online User Intentions and Behavior,” CroDiM (1:1), pp. 57–67.
    Blom, J. 2000. “Personalization - A Taxonomy,” Conference on Human Factors in Computing Systems - Proceedings (April), pp. 313–314.
    Bo, X., and Benbasat, I. 2007. “E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact,” MIS Quarterly: Management Information Systems.
    Bol, N., Høie, N. M., Nguyen, M. H., and Smit, E. S. 2019. “Customization in Mobile Health Apps: Explaining Effects on Physical Activity Intentions by the Need for Autonomy,” Digital Health (5), pp. 1–12.
    Burke, R. 2002. “Hybrid Recommender Systems: Survey and Experiments,” User Modelling and User-Adapted Interaction.
    Chen, G., and Kotz, D. 2000. “A Survey of Context-Aware Mobile Computing Research [Un Estudio de La Investigación Sobre Computación Móvil Sensible Al Contexto],” Computer Science Technical Reports (1:2.1), pp. 1–16. (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.140.3131&rep=rep1&type=pdf).
    Fischer, G. 1993. “Shared Knowledge in Cooperative Problem-Solving Systems-Integrating Adaptive and Adaptable Components.”
    Fulgoni, G. M. 2016. “In the Digital World, Not Everything That Can Be Measured Matters,” Journal of Advertising Research.
    Gullà, F., Ceccacci, S., Germani, M., and Cavalieri, L. 2015. “Design Adaptable and Adaptive User Interfaces: A Method to Manage the Information,” Biosystems and Biorobotics (11:September), pp. 47–58.
    Guo, Y., Cheng, Z., Nie, L., Wang, Y., Ma, J., and Kankanhalli, M. 2018. “Attentive Long Short-Term Preference Modeling for Personalized Product Search,” ACM Transactions on Information Systems (37:2).
    Hayakawa, M. 2009. “Matrix Factorization Techniques for Recommender System,” Earthquake Prediction with Radio Techniques, pp. 199–207.
    Hook, K. 1998. “Evaluating the Utility and Usability Adaptive Hypermedia System.” (www.sits.sel-kial).
    Jørgensen, L. 2017. I Want to Show-How User-Centered Design Methods Can Assist When Preparing for Micro Moments., (December). (http://www.youtube.com/watch?v=eiR2t-h537I&feature=youtube_gdata).
    Jugovac, M., Jannach, D., and Dortmund, T. U. 2017. “Interacting with Recommenders — Overview and Research Directions,” ACM Transaction on Interactive Intelligent Systems (7:3).
    Jung, J. H., Schneider, C., and Valacich, J. 2010. “Enhancing the Motivational Affordance of Information Systems: The Effects of Real-Time Performance Feedback and Goal Setting in Group Collaboration Environments,” Management Science (56:4), pp. 724–742.
    Kim, Y. S., Kim, S., Cho, Y. J., and Park, S. H. 2005. Adaptive Customization of User Interface Design Based on Learning Styles and Behaviors : A Case Study of a Heritage Alive Learning System, pp. 1–5.
    Knijnenburg, B. P., and Willemsen, M. C. 2009. “Understanding the Effect of Adaptive Preference Elicitation Methods on User Satisfaction of a Recommender System,” RecSys’09 - Proceedings of the 3rd ACM Conference on Recommender Systems, pp. 381–384.
    Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., and Newell, C. 2012. “Explaining the User Experience of Recommender Systems,” User Modeling and User-Adapted Interaction.
    Kornilova, O. 2012. Adaptive User Interface Patterns for Mobile Applications, pp. 2012–2013.
    Kwon, K., and Kim, C. 2012. “How to Design Personalization in a Context of Customer Retention: Who Personalizes What and to What Extent?,” Electronic Commerce Research and Applications (11:2), Elsevier B.V., pp. 101–116.
    Lavie, T., and Meyer, J. 2010. “Benefits and Costs of Adaptive User Interfaces,” International Journal of Human Computer Studies (68:8), Elsevier, pp. 508–524.
    Lewis, J. R. 1995. “IBM Computer Usability Satisfaction Questionnaires: Psychometric Evaluation and Instructions for Use,” International Journal of Human-Computer Interaction (7:1), pp. 57–78.
    Liu, Q., and Gan, X. 2016. “Combining User Contexts and User Opinions for Restaurant Recommendation in Mobile Environment,” Journal of Electronic Commerce in Organizations (14:1), pp. 45–63.
    McNee, S. M., Riedl, J., and Konstan, J. A. 2006. “Being Accurate Is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems,” in Conference on Human Factors in Computing Systems - Proceedings.
    McStay, A. 2017. “Micro-Moments, Liquidity, Intimacy and Automation: Developments in Programmatic Ad-Tech,” Commercial Communication in the Digital Age, pp. 143–160.
    Miller, K. A., Deci, E. L., and Ryan, R. M. 1988. “Intrinsic Motivation and Self-Determination in Human Behavior,” Contemporary Sociology.
    Murray, K. B., and Häubl, G. 2008. “Interactive Consumer Decision Aids,” in International Series in Operations Research and Management Science.
    Ozok, A. A., Fan, Q., and Norcio, A. F. 2010. “Design Guidelines for Effective Recommender System Interfaces Based on a Usability Criteria Conceptual Model: Results from a College Student Population,” Behaviour and Information Technology.
    Peissner, M., and Sellner, T. 2012. “Transparency and Controllability in User Interfaces That Adapt during Run-Time,” Workshop on End-User Interactions with Intelligent and Autonomous Systems. ACM.
    Pu, P., and Chen, L. 2007. “Trust-Inspiring Explanation Interfaces for Recommender Systems,” Knowledge-Based Systems.
    Pu, P., Chen, L., and Hu, R. 2012. “Evaluating Recommender Systems from the User’s Perspective: Survey of the State of the Art,” User Modeling and User-Adapted Interaction.
    Ramaswamy, S. 2015. “How Micro-Moments Are Changing the Rules,” Think With Google. (https://www.thinkwithgoogle.com/marketing-resources/micro-moments/how-micromoments-are-changing-rules/).
    Reeve, J. 2013. “Understanding Motivation and Emotion Fifth Edition,” John Wiley & Sons, Inc.
    Schein, A. I., Popescul, A., Ungar, L. H., and Pennock, D. M. 2002. “Methods and Metrics for Cold-Start Recommendations,” SIGIR Forum (ACM Special Interest Group on Information Retrieval) (August), pp. 253–260.
    Stokes, P., and Harris, P. 2012. “Micro-Moments, Choice and Responsibility in Sustainable Organizational Change and Transformation: The Janus Dialectic,” Journal of Organizational Change Management.
    Te’eni, D., Carey, J., and Zhang, P. 2007. Human-Computer Interaction: Developing Effective Organizational Information Systems.
    Trumbly, J. E., Arnett, K. P., and Johnson, P. C. 1994. “Productivity Gains via an Adaptive User Interface: An Empirical Analysis,” International Journal of Human - Computer Studies (40:1), Academic Press, pp. 63–81.
    Vignoles, V. L., Chryssochoou, X., and Breakwell, G. M. 2000. The Distinctiveness Principle: Identity, Meaning, and the Bounds of Cultural Relativity, (4:4), pp. 337–354.
    Voorveld, H., Neijens, P., and Smit, E. 2011. “The Relation between Actual and Perceived Interactivity: What Makes the Web Sites of Top Global Brands Truly Interactive?,” Journal of Advertising (40:2), pp. 77–92.
    Wang, D., Park, S., and Fesenmaier, D. R. 2012. “The Role of Smartphones in Mediating the Touristic Experience,” Journal of Travel Research.
    Weld, D. S., Anderson, C., Domingos, P., Etzioni, O., Gajos, K., Lau, T., and Wolfman, S. 2003. “Automatically Personalizing User Interfaces,” IJCAI International Joint Conference on Artificial Intelligence, pp. 1613–1619.
    Zeidler, C., Lutteroth, C., and Weber, G. 2013. “An Evaluation of Advanced User Interface Customization,” Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration, OzCHI 2013, pp. 295–304.
    Zeng, J., Li, F., Liu, H., Wen, J., and Hirokawa, S. 2016. “A Restaurant Recommender System Based on User Preference and Location in Mobile Environment,” Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (2015), pp. 55–60.
    Zhang, P. 2008a. “Motivational Affordances: Reasons for ICT Design and Use,” Communications of the ACM (51:11), pp. 145–147.
    Zhang, P. 2008b. Toward a Positive Design Theory: Principle for Designing Motivating Information and Communication Technology.
    Ziegler, C.-N., McNee, S. M., Konstan, J. A., and Lausen, G. 2005. Improving Recommendation Lists through Topic Diversification.
    Zimmermann, A., Lorenz, A., and Oppermann, R. 2007. “An Operational Definition of Context,” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (4635 LNAI), pp. 558–571.
    描述: 碩士
    國立政治大學
    資訊管理學系
    108356025
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0108356025
    数据类型: thesis
    DOI: 10.6814/NCCU202101336
    显示于类别:[資訊管理學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    602501.pdf1585KbAdobe PDF20检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈