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題名 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
貢獻者 林怡伶
Lin, Yi-Ling
李紹威
Lee, Shao-Wei
關鍵詞 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
上傳時間 2-Sep-2021 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/).
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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
資料類型 thesis
dc.contributor.advisor 林怡伶zh_TW
dc.contributor.advisor Lin, Yi-Lingen_US
dc.contributor.author (Authors) 李紹威zh_TW
dc.contributor.author (Authors) Lee, Shao-Weien_US
dc.creator (作者) 李紹威zh_TW
dc.creator (作者) Lee, Shao-Weien_US
dc.date (日期) 2021en_US
dc.date.accessioned 2-Sep-2021 15:54:54 (UTC+8)-
dc.date.available 2-Sep-2021 15:54:54 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2021 15:54:54 (UTC+8)-
dc.identifier (Other Identifiers) G0108356025en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136847-
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) 108356025zh_TW
dc.description.abstract (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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents Acknowledgement I
摘要 II
Abstract III
Tables i
Figures ii
Chapter 1 Introduction 1
Chapter 2 Literature Review 5
2-1 CONTEXT 5
2-2 MICRO-MOMENTS 5
2-3 MICRO-MOMENT RECOMMENDER SYSTEM 6
2-4 MOTIVATIONAL AFFORDANCE 7
Chapter 3 Research Framework and Development 9
Chapter 4 Methodology 14
4-1 DATASET 14
4-2 TASKS 14
4-3 RECOMMENDATION ALGORITHM 16
4-4 APP SYSTEM 17
4-5 DESIGN & PROCEDURE 22
4-6 PARTICIPANTS 25
4-7 HYPOTHESIS AND STATISTIC METHOD 26
Chapter 5 Analysis and result 28
5-1 ANALYSIS OF PRELIMINARY SURVEY 28
5-2 ANALYSIS OF ONBOARDING SURVEY 29
5-3 ANALYSIS OF APP LOG 31
5-4 ANALYSIS OF POST SURVEY 34
Chapter 6 Discussion and conclusion 40
6-1 DISCUSSION 40
6-2 THEORETICAL IMPLICATIONS 42
6-3 PRACTICAL IMPLICATIONS 43
6-4 LIMITATIONS AND FUTURE WORK 44
Reference 45
Appendix A - Preliminary survey 51
ENGLISH VERSION. 51
MANDARIN VERSION. 52
Appendix B – Onboarding survey 55
ENGLISH VERSION. 55
MANDARIN VERSION. 56
Appendix C – Post survey 59
ENGLISH VERSION. 59
MANDARIN VERSION. 60		zh_TW
dc.format.extent 1623146 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108356025en_US
dc.subject (關鍵詞) 微時刻推薦系統zh_TW
dc.subject (關鍵詞) 個人化zh_TW
dc.subject (關鍵詞) 動機賦能zh_TW
dc.subject (關鍵詞) 互動機制zh_TW
dc.subject (關鍵詞) Micro-moment recommender systemen_US
dc.subject (關鍵詞) Personalizationen_US
dc.subject (關鍵詞) motivational affordanceen_US
dc.subject (關鍵詞) Interactive mechanismen_US
dc.title (題名) 以個人化建構微時刻推薦系統的互動機制zh_TW
dc.title (題名) A personalized Interactive Mechanism Framework for Micro-moment Recommender Systemen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (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.
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dc.identifier.doi (DOI) 10.6814/NCCU202101336en_US