政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152406

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/152406

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文笔数/总笔数 : 113160/144130 (79%)
造访人次 : 50755335 在线人数 : 714

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

主页 ‧ 登入 ‧ 上传 ‧ 说明 ‧ 关于政大典藏 ‧ 管理

到手机版

政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/152406

请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/152406

题名:	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
贡献者:	林怡伶 Lin, Yi-Ling 鄭宇翔 Zheng, Yu-Xiang
关键词:	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
上传时间:	2024-08-05 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
数据类型:	thesis
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
600401.pdf		1551Kb	Adobe PDF	0	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