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題名 微時刻推薦系統機制設計－社會關係與偶然驚喜之影響
The Influence of Social Relationships and Serendipity on Micro-Moment Recommender System
作者 鄭宇翔
Zheng, Yu-Xiang
貢獻者 林怡伶
Lin, Yi-Ling
鄭宇翔
Zheng, Yu-Xiang
關鍵詞 團體推薦系統
社會關係
偶然驚喜
微時刻
Group recommendation system
Social relationship
Serendipity
Micro-moments
日期 2023
上傳時間 5-Aug-2024 12:06:10 (UTC+8)
摘要 網路的快速發展使線上資訊變得更複雜，同時行動裝置和社交活動也日益盛行，將我們的日常生活分割成許多微時刻。推薦系統能夠在微時刻內即時根據使用者的脈絡和意圖提供推薦，從而解決資訊過量的問題。隨著我們在日常生活中越來越頻繁地與不同社會關係的人進行團體活動，團體推薦系統的重要性也日益增加。然而，我們在微時刻中經常有頻繁的推薦系統使用需求，導致過度專業化的問題，而偶然驚喜是一種解決此問題並提高使用者滿意度的元素。因此，本研究旨在提出一種考量社會關係脈絡及偶然驚喜意圖的微時刻團體推薦系統，並探討社會關係對用戶滿意度、行為意圖和偶然驚喜的影響。本研究實際開發了一款新型的微時刻推薦系統，並招募受測者進行為期兩週的實地研究。實驗結果證明，在微時刻推薦系統中加入基於社會關係的團體推薦機制和偶然驚喜機制是可行且有效的，同時也提升使用者在微時刻下的滿意度和行為意圖。
With the increase in mobile devices and social activities, our life has been divided into micro-moments, and we engage in more group activities with different social relationship people. In addition to individual recommender systems, group recommender systems are becoming more important. However, people tend to request frequent recommendations in micro-moments which is prone to overspecialization problems. Serendipity is a way to solve this problem and improve user satisfaction. Therefore, this study aims to propose a micro-moment recommender system that focuses on the context of social relationships and the intention of serendipity. From a social perspective, this study investigates the effects of social relationships on user satisfaction, behavioral intentions, and serendipity. This study developed a new micro-moment recommender system and conducted a field study for two weeks. The result demonstrates the feasibility and effectiveness of incorporating a group recommendation mechanism that considers social relationships and a serendipity mechanism in a micro-moment recommender system. This study emphasizes the importance of considering group recommendations based on social relationships and serendipitous recommendations to enhance user satisfaction and behavior intentions in micro-moments.
參考文獻 Adams, L., Burkholder, E., & Hamilton, K. (2015). Micro-Moments: Your Guide to Winning the Shift to Mobile. Think with Google. https://think.storage.googleapis.com/images/micromoments- guide-to-winning-shift-to-mobile-download.pdf Auty, S. (1992). Consumer choice and segmentation in the restaurant industry. Service Industries Journal, 12(3), 324-339. Ayres, J. (1979). Uncertainty and social penetration theory expectations about relationship communication: A comparative test. Western Journal of Communication (Includes Communication Reports), 43(3), 192-200. Ballinger, G. A. (2004). Using generalized estimating equations for longitudinal data analysis. Organizational research methods, 7(2), 127-150. Baltrunas, L., Makcinskas, T., & Ricci, F. (2010). Group recommendations with rank aggregation and collaborative filtering. Proceedings of the fourth ACM conference on Recommender systems, 199-126. Biloš, A., Turkalj, D., & Kelić, I. (2018). Micro-moments of user experience: An approach to understanding online user intentions and behavior. CroDiM: International Journal of Marketing Science, 1(1), 57-67. Cavalinhos, S., Marques, S. H., & Fátima Salgueiro, M. (2021). The use of mobile devices in‐store and the effect on shopping experience: A systematic literature review and research agenda. International Journal of Consumer Studies, 45(6), 1198-1216. https://doi.org/10.1111/ijcs.12690 Celma Herrada, Ò. (2009). Music recommendation and discovery in the long tail. Universitat Pompeu Fabra. Chen, L., & Xia, M. (2021). A context-aware recommendation approach based on feature selection. Applied Intelligence, 51, 865-875. Chen, L., Yang, Y., Wang, N., Yang, K., & Yuan, Q. (2019). How serendipity improves user satisfaction with recommendations? a large-scale user evaluation. The world wide web conference, 240-250. Christensen, I., Schiaffino, S., & Armentano, M. (2016). Social group recommendation in the tourism domain. Journal of intelligent information systems, 47(2), 209-231. Dara, S., Chowdary, C. R., & Kumar, C. (2020). A survey on group recommender systems. Journal of Intelligent Information Systems, 54(2), 271-295. del Carmen Rodríguez-Hernández, M., & Ilarri, S. (2021). AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions. Knowledge-Based Systems, 215, 106740. Deliens, T., Clarys, P., De Bourdeaudhuij, I., & Deforche, B. (2014). Determinants of eating behaviour in university students: a qualitative study using focus group discussions. BMC Public Health, 14(1), 1-12. 66 Forsyth, D. R. (2018). Group dynamics. Cengage Learning. Gabriel, S., Renaud, J. M., & Tippin, B. (2007). When I think of you, I feel more confident about me: The relational self and self-confidence. Journal of Experimental Social Psychology, 43(5), 772-779. Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., & Seada, K. (2010). Enhancing group recommendation by incorporating social relationship interactions. Proceedings of the 16th ACM international conference on Supporting group work, 97-106. Han, S. P., Ghose, A., & Xu, K. (2013). Mobile commerce in the new tablet economy. Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The adaptive web: methods and strategies of web personalization (pp. 596-627). Springer. Jiang, L., Cheng, Y., Yang, L., Li, J., Yan, H., & Wang, X. (2019). A trust-based collaborative filtering algorithm for E-commerce recommendation system. Journal of Ambient Intelligence and Humanized Computing, 10(8), 3023-3034. Kompan, M., & Bielikova, M. (2014). Group recommendations: Survey and perspectives. Computing and Informatics, 33(2), 446-476. Kotkov, D., Veijalainen, J., & Wang, S. (2016). Challenges of serendipity in recommender systems. International conference on web information systems and technologies. Kotkov, D., Veijalainen, J., & Wang, S. (2017). A serendipity-oriented greedy algorithm for recommendations. International conference on web information systems and technologies. Kotkov, D., Veijalainen, J., & Wang, S. (2020). How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing, 102(2), 393-411. Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180-192. Lin, Y.-L., & Ding, N.-D. (2023). Competitive gamification in crowdsourcing-based contextual-aware recommender systems. International Journal of Human-Computer Studies, 103083. Lin, Y.-L., & Lee, S.-W. (2023). A Personalized Interaction Mechanism Framework for Micro- moment Recommender Systems. ACM Transactions on Interactive Intelligent Systems, 13(1), 1-28. Lv, J., & Liu, X. (2022). The Impact of Information Overload of E-Commerce Platform on Consumer Return Intention: Considering the Moderating Role of Perceived Environmental Effectiveness. Int J Environ Res Public Health, 19(13). https://doi.org/10.3390/ijerph19138060 Maccatrozzo, V., Terstall, M., Aroyo, L., & Schreiber, G. (2017). SIRUP: Serendipity in recommendations via user perceptions. Proceedings of the 22nd International Conference on Intelligent User Interfaces, 35-44. Maksai, A., Garcin, F., & Faltings, B. (2015). Predicting online performance of news recommender systems through richer evaluation metrics. Proceedings of the 9th ACM Conference on Recommender Systems, 179-186. Matt, C., Benlian, A., Hess, T., & Weiß, C. (2014). Escaping from the filter bubble? The effects of 67 novelty and serendipity on users’ evaluations of online recommendations. McCarthy, J. (2002). Pocket restaurant finder: a situated recommendation systems for groups. Proceeding on ACM Conf. on Human Factors in Computer Systems. McCarthy, J. F., & Anagnost, T. D. (1998). MusicFX: an arbiter of group preferences for computer supported collaborative workouts. Proceedings of the 1998 ACM conference on Computer supported cooperative work, 363-372. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 415-444. McStay, A. (2017). Micro-moments, liquidity, intimacy and automation: Developments in programmatic ad-tech. In Commercial communication in the digital age–information or disinformation? (pp. 143-159). Mouton de Gruyter. Olshannikova, E., Olsson, T., Huhtamäki, J., Paasovaara, S., & Kärkkäinen, H. (2020). From chance to serendipity: knowledge workers’ experiences of serendipitous social encounters. Advances in Human-Computer Interaction, 2020. Pérez, L. G., Mata, F., Chiclana, F., Kou, G., & Herrera-Viedma, E. (2016). Modelling influence in group decision making. Soft Computing, 20(4), 1653-1665. Pu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. Proceedings of the fifth ACM conference on Recommender systems, Quijano-Sanchez, L., Recio-Garcia, J. A., & Diaz-Agudo, B. (2010). Personality and social trust in group recommendations. 2010 22Nd IEEE international conference on tools with artificial intelligence, 121-126. Singh, S., & Jang, S. (2020). Search, purchase, and satisfaction in a multiple-channel environment: how have mobile devices changed consumer behaviors? Journal of Retailing and Consumer Services, 102200. Stokes, P., Millar, C., & Harris, P. (2012). Micro‐moments, choice and responsibility in sustainable organizational change and transformation. Journal of Organizational Change Management, 25(4), 595-611. https://doi.org/10.1108/09534811211239245 Sun, Y., Yuan, N. J., Xie, X., McDonald, K., & Zhang, R. (2017). Collaborative Intent Prediction with Real-Time Contextual Data. ACM Transactions on Information Systems, 35(4), 1-33. https://doi.org/10.1145/3041659 Turner, A. (2015). Generation Z: Technology and social interest. The Journal of Individual Psychology, 71(2), 103-113. Vanhamme, J., Lindgreen, A., & Beverland, M. (2020). The paradox of surprise: empirical evidence about surprising gifts received and given by close relations. European Journal of Marketing. Wang, N., & Chen, L. (2022). How Do Item Features and User Characteristics Affect Users' Perceptions of Recommendation Serendipity? A Cross-Domain Analysis. User Modeling and User-Adapted Interaction, 1-39. Zhang, Y. C., Séaghdha, D. Ó., Quercia, D., & Jambor, T. (2012). Auralist: introducing serendipity 68 into music recommendation. Proceedings of the fifth ACM international conference on Web search and data mining, 13-22. Zheng, Q., Chan, C.-K., & Ip, H. H. (2015). An unexpectedness-augmented utility model for making serendipitous recommendation. Advances in Data Mining: Applications and Theoretical Aspects: 15th Industrial Conference, ICDM 2015, Hamburg, Germany, July 11-24, 2015, Proceedings 15, 216-230. Ziarani, R. J., & Ravanmehr, R. (2021). Serendipity in recommender systems: a systematic literature review. Journal of Computer Science and Technology, 36(2), 375-396.
描述 碩士
國立政治大學
資訊管理學系
110356004
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110356004
資料類型 thesis
dc.contributor.advisor 林怡伶zh_TW
dc.contributor.advisor Lin, Yi-Lingen_US
dc.contributor.author (Authors) 鄭宇翔zh_TW
dc.contributor.author (Authors) Zheng, Yu-Xiangen_US
dc.creator (作者) 鄭宇翔zh_TW
dc.creator (作者) Zheng, Yu-Xiangen_US
dc.date (日期) 2023en_US
dc.date.accessioned 5-Aug-2024 12:06:10 (UTC+8)-
dc.date.available 5-Aug-2024 12:06:10 (UTC+8)-
dc.date.issued (上傳時間) 5-Aug-2024 12:06:10 (UTC+8)-
dc.identifier (Other Identifiers) G0110356004en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/152406-
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) 110356004zh_TW
dc.description.abstract (摘要) 網路的快速發展使線上資訊變得更複雜，同時行動裝置和社交活動也日益盛行，將我們的日常生活分割成許多微時刻。推薦系統能夠在微時刻內即時根據使用者的脈絡和意圖提供推薦，從而解決資訊過量的問題。隨著我們在日常生活中越來越頻繁地與不同社會關係的人進行團體活動，團體推薦系統的重要性也日益增加。然而，我們在微時刻中經常有頻繁的推薦系統使用需求，導致過度專業化的問題，而偶然驚喜是一種解決此問題並提高使用者滿意度的元素。因此，本研究旨在提出一種考量社會關係脈絡及偶然驚喜意圖的微時刻團體推薦系統，並探討社會關係對用戶滿意度、行為意圖和偶然驚喜的影響。本研究實際開發了一款新型的微時刻推薦系統，並招募受測者進行為期兩週的實地研究。實驗結果證明，在微時刻推薦系統中加入基於社會關係的團體推薦機制和偶然驚喜機制是可行且有效的，同時也提升使用者在微時刻下的滿意度和行為意圖。zh_TW
dc.description.abstract (摘要) With the increase in mobile devices and social activities, our life has been divided into micro-moments, and we engage in more group activities with different social relationship people. In addition to individual recommender systems, group recommender systems are becoming more important. However, people tend to request frequent recommendations in micro-moments which is prone to overspecialization problems. Serendipity is a way to solve this problem and improve user satisfaction. Therefore, this study aims to propose a micro-moment recommender system that focuses on the context of social relationships and the intention of serendipity. From a social perspective, this study investigates the effects of social relationships on user satisfaction, behavioral intentions, and serendipity. This study developed a new micro-moment recommender system and conducted a field study for two weeks. The result demonstrates the feasibility and effectiveness of incorporating a group recommendation mechanism that considers social relationships and a serendipity mechanism in a micro-moment recommender system. This study emphasizes the importance of considering group recommendations based on social relationships and serendipitous recommendations to enhance user satisfaction and behavior intentions in micro-moments.en_US
dc.description.tableofcontents Chapter 1 Introduction 9 Chapter 2 Literature Review 12 2.1 Micro-moments 12 2.2 Group recommender system 13 2.3 Serendipity 14 Chapter 3 Background and Research Development 16 Chapter 4 Proposed Recommender System 19 4.1 System design 19 4.2 Recommendation generation 22 4.2.1 Group decision strategy 23 4.2.2 Recommendation algorithm 24 4.2.3 Serendipity-oriented algorithm 25 4.2.4 Post-filtering 26 Chapter 5 Methodology 27 5.1 Domain 27 5.2 Dataset 27 5.3 Tasks 28 5.4 Experiment design and procedure 28 5.4.1 Onboarding survey 28 5.4.2 Field Study 28 5.4.3 Post-Survey 29 5.5 Participants 29 5.6 Measurement 31 5.6.1 System Logs 32 5.6.2 Questionnaire 33 Chapter 6 Analysis and Result 34 6.1 Analysis of the preliminary study 34 6.2 Analysis of the onboarding survey 36 6.3 Analysis of system logs 37 6.3.1 Compared system logs based on system designs 38 6.3.2 Compared system logs based on the usage of the serendipity mechanism 43 6.3.3 The interaction between social relationships and the serendipity mechanism 45 6.3.4 Compared system logs based on gender in different groups 47 6.3.5 Compared system logs based on individual and group recommendation mechanisms 50 6.4 Analysis of post-survey 52 6.4.1 Reliability and validity of the structural model 52 6.4.2 Analysis of the structural model 54 6.4.3 Analysis of system design 57 6.4.4 Analysis of the serendipity mechanism 58 6.4.5 Analysis of serendipity mechanism and social relationships 58 Chapter 7 Discussion and Conclusion 60 7.1 Group recommendation mechanism in a MMRS 60 7.2 Serendipity mechanism in a MMRS 62 7.3 Social relationships and the serendipity mechanism 63 7.4 Theoretical contributions 64 7.5 Practical contributions 65 7.6 Limitations and future work 66 Reference 67 Appendix 71zh_TW
dc.format.extent 1588966 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110356004en_US
dc.subject (關鍵詞) 團體推薦系統zh_TW
dc.subject (關鍵詞) 社會關係zh_TW
dc.subject (關鍵詞) 偶然驚喜zh_TW
dc.subject (關鍵詞) 微時刻zh_TW
dc.subject (關鍵詞) Group recommendation systemen_US
dc.subject (關鍵詞) Social relationshipen_US
dc.subject (關鍵詞) Serendipityen_US
dc.subject (關鍵詞) Micro-momentsen_US
dc.title (題名) 微時刻推薦系統機制設計－社會關係與偶然驚喜之影響zh_TW
dc.title (題名) The Influence of Social Relationships and Serendipity on Micro-Moment Recommender Systemen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Adams, L., Burkholder, E., & Hamilton, K. (2015). Micro-Moments: Your Guide to Winning the Shift to Mobile. Think with Google. https://think.storage.googleapis.com/images/micromoments- guide-to-winning-shift-to-mobile-download.pdf Auty, S. (1992). Consumer choice and segmentation in the restaurant industry. Service Industries Journal, 12(3), 324-339. Ayres, J. (1979). Uncertainty and social penetration theory expectations about relationship communication: A comparative test. Western Journal of Communication (Includes Communication Reports), 43(3), 192-200. Ballinger, G. A. (2004). Using generalized estimating equations for longitudinal data analysis. Organizational research methods, 7(2), 127-150. Baltrunas, L., Makcinskas, T., & Ricci, F. (2010). Group recommendations with rank aggregation and collaborative filtering. Proceedings of the fourth ACM conference on Recommender systems, 199-126. Biloš, A., Turkalj, D., & Kelić, I. (2018). Micro-moments of user experience: An approach to understanding online user intentions and behavior. CroDiM: International Journal of Marketing Science, 1(1), 57-67. Cavalinhos, S., Marques, S. H., & Fátima Salgueiro, M. (2021). The use of mobile devices in‐store and the effect on shopping experience: A systematic literature review and research agenda. International Journal of Consumer Studies, 45(6), 1198-1216. https://doi.org/10.1111/ijcs.12690 Celma Herrada, Ò. (2009). Music recommendation and discovery in the long tail. Universitat Pompeu Fabra. Chen, L., & Xia, M. (2021). A context-aware recommendation approach based on feature selection. Applied Intelligence, 51, 865-875. Chen, L., Yang, Y., Wang, N., Yang, K., & Yuan, Q. (2019). How serendipity improves user satisfaction with recommendations? a large-scale user evaluation. The world wide web conference, 240-250. Christensen, I., Schiaffino, S., & Armentano, M. (2016). Social group recommendation in the tourism domain. Journal of intelligent information systems, 47(2), 209-231. Dara, S., Chowdary, C. R., & Kumar, C. (2020). A survey on group recommender systems. Journal of Intelligent Information Systems, 54(2), 271-295. del Carmen Rodríguez-Hernández, M., & Ilarri, S. (2021). AI-based mobile context-aware recommender systems from an information management perspective: Progress and directions. Knowledge-Based Systems, 215, 106740. Deliens, T., Clarys, P., De Bourdeaudhuij, I., & Deforche, B. (2014). Determinants of eating behaviour in university students: a qualitative study using focus group discussions. BMC Public Health, 14(1), 1-12. 66 Forsyth, D. R. (2018). Group dynamics. Cengage Learning. Gabriel, S., Renaud, J. M., & Tippin, B. (2007). When I think of you, I feel more confident about me: The relational self and self-confidence. Journal of Experimental Social Psychology, 43(5), 772-779. Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., & Seada, K. (2010). Enhancing group recommendation by incorporating social relationship interactions. Proceedings of the 16th ACM international conference on Supporting group work, 97-106. Han, S. P., Ghose, A., & Xu, K. (2013). Mobile commerce in the new tablet economy. Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The adaptive web: methods and strategies of web personalization (pp. 596-627). Springer. Jiang, L., Cheng, Y., Yang, L., Li, J., Yan, H., & Wang, X. (2019). A trust-based collaborative filtering algorithm for E-commerce recommendation system. Journal of Ambient Intelligence and Humanized Computing, 10(8), 3023-3034. Kompan, M., & Bielikova, M. (2014). Group recommendations: Survey and perspectives. Computing and Informatics, 33(2), 446-476. Kotkov, D., Veijalainen, J., & Wang, S. (2016). Challenges of serendipity in recommender systems. International conference on web information systems and technologies. Kotkov, D., Veijalainen, J., & Wang, S. (2017). A serendipity-oriented greedy algorithm for recommendations. International conference on web information systems and technologies. Kotkov, D., Veijalainen, J., & Wang, S. (2020). How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm. Computing, 102(2), 393-411. Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180-192. Lin, Y.-L., & Ding, N.-D. (2023). Competitive gamification in crowdsourcing-based contextual-aware recommender systems. International Journal of Human-Computer Studies, 103083. Lin, Y.-L., & Lee, S.-W. (2023). A Personalized Interaction Mechanism Framework for Micro- moment Recommender Systems. ACM Transactions on Interactive Intelligent Systems, 13(1), 1-28. Lv, J., & Liu, X. (2022). The Impact of Information Overload of E-Commerce Platform on Consumer Return Intention: Considering the Moderating Role of Perceived Environmental Effectiveness. Int J Environ Res Public Health, 19(13). https://doi.org/10.3390/ijerph19138060 Maccatrozzo, V., Terstall, M., Aroyo, L., & Schreiber, G. (2017). SIRUP: Serendipity in recommendations via user perceptions. Proceedings of the 22nd International Conference on Intelligent User Interfaces, 35-44. Maksai, A., Garcin, F., & Faltings, B. (2015). Predicting online performance of news recommender systems through richer evaluation metrics. Proceedings of the 9th ACM Conference on Recommender Systems, 179-186. Matt, C., Benlian, A., Hess, T., & Weiß, C. (2014). Escaping from the filter bubble? The effects of 67 novelty and serendipity on users’ evaluations of online recommendations. McCarthy, J. (2002). Pocket restaurant finder: a situated recommendation systems for groups. Proceeding on ACM Conf. on Human Factors in Computer Systems. McCarthy, J. F., & Anagnost, T. D. (1998). MusicFX: an arbiter of group preferences for computer supported collaborative workouts. Proceedings of the 1998 ACM conference on Computer supported cooperative work, 363-372. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 415-444. McStay, A. (2017). Micro-moments, liquidity, intimacy and automation: Developments in programmatic ad-tech. In Commercial communication in the digital age–information or disinformation? (pp. 143-159). Mouton de Gruyter. Olshannikova, E., Olsson, T., Huhtamäki, J., Paasovaara, S., & Kärkkäinen, H. (2020). From chance to serendipity: knowledge workers’ experiences of serendipitous social encounters. Advances in Human-Computer Interaction, 2020. Pérez, L. G., Mata, F., Chiclana, F., Kou, G., & Herrera-Viedma, E. (2016). Modelling influence in group decision making. Soft Computing, 20(4), 1653-1665. Pu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. Proceedings of the fifth ACM conference on Recommender systems, Quijano-Sanchez, L., Recio-Garcia, J. A., & Diaz-Agudo, B. (2010). Personality and social trust in group recommendations. 2010 22Nd IEEE international conference on tools with artificial intelligence, 121-126. Singh, S., & Jang, S. (2020). Search, purchase, and satisfaction in a multiple-channel environment: how have mobile devices changed consumer behaviors? Journal of Retailing and Consumer Services, 102200. Stokes, P., Millar, C., & Harris, P. (2012). Micro‐moments, choice and responsibility in sustainable organizational change and transformation. Journal of Organizational Change Management, 25(4), 595-611. https://doi.org/10.1108/09534811211239245 Sun, Y., Yuan, N. J., Xie, X., McDonald, K., & Zhang, R. (2017). Collaborative Intent Prediction with Real-Time Contextual Data. ACM Transactions on Information Systems, 35(4), 1-33. https://doi.org/10.1145/3041659 Turner, A. (2015). Generation Z: Technology and social interest. The Journal of Individual Psychology, 71(2), 103-113. Vanhamme, J., Lindgreen, A., & Beverland, M. (2020). The paradox of surprise: empirical evidence about surprising gifts received and given by close relations. European Journal of Marketing. Wang, N., & Chen, L. (2022). How Do Item Features and User Characteristics Affect Users' Perceptions of Recommendation Serendipity? A Cross-Domain Analysis. User Modeling and User-Adapted Interaction, 1-39. Zhang, Y. C., Séaghdha, D. Ó., Quercia, D., & Jambor, T. (2012). Auralist: introducing serendipity 68 into music recommendation. Proceedings of the fifth ACM international conference on Web search and data mining, 13-22. Zheng, Q., Chan, C.-K., & Ip, H. H. (2015). An unexpectedness-augmented utility model for making serendipitous recommendation. Advances in Data Mining: Applications and Theoretical Aspects: 15th Industrial Conference, ICDM 2015, Hamburg, Germany, July 11-24, 2015, Proceedings 15, 216-230. Ziarani, R. J., & Ravanmehr, R. (2021). Serendipity in recommender systems: a systematic literature review. Journal of Computer Science and Technology, 36(2), 375-396.zh_TW