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題名 兩階段動態團體推薦系統：從個人到團體決策的動態適應機制
A Two-Stage Dynamic Group Recommendation System: Dynamic Adaptation from Individual to Group Decision-Making
作者 蔡凱亘
Tsai, Kai-Hsuan
貢獻者 林怡伶
Lin, Yi-Ling
蔡凱亘
Tsai, Kai-Hsuan
關鍵詞 團體推薦
動態機制
團體決策
Group Recommendation
Dynamic Mechanism
Group Decision-Making
日期 2025
上傳時間 1-Sep-2025 15:03:44 (UTC+8)
摘要 團體推薦系統常常面臨資料稀少、偏好動態偏移以及難以達成團體共識等挑戰。本研究遵循設計科學的方法，提出了兩階段動態團體推薦系統，藉由兩階段的設計以及能夠捕捉使用者即時情境和意圖的動態機制，從而實現個人化且符合團體目的的團體推薦，且無需依賴團體的歷史資料。在第一階段，使用者根據當下的情境篩選器接收個人化推薦；在第二階段，這些推薦被聚合形成最終的團體推薦。本系統設計同時融合了兩種動態機制：由群組創建者擔任代表，基於領導者的推薦機制 (LBD) 和允許所有成員參與貢獻，基於個體的推薦機制 (IDD)。實驗結果表明，兩種機制均能提供令人滿意且目標一致的推薦結果，但使用者普遍更喜歡 IDD 機制，認為其具有更高的靈活性、參與度和多樣性。本研究證明了將動態的使用者輸入整合到輕量級、使用者導向的設計中，使團體推薦系統能夠更好地適應團體需求並增強決策體驗。
Group Recommendation Systems (GRSs) often face challenges such as data sparsity, dynamic preference shifts, and difficulty in reaching group consensus. Following the design science approach, this study proposes a two-stage dynamic GRS. Through a two-stage design and a dynamic mechanism that can capture users' immediate context and intent, it can achieve personalized and group-purpose group recommendations without relying on the historical group data. In the first stage, users receive individual recommendations based on current contextual filters; in the second stage, these are aggregated to form a final group recommendation. The system design incorporates two dynamic mechanisms: leader-based (LBD) where the group creator acts as the representative and sets the filter, and individual-driven (IDD) that allows all members to participate in expressing preferences. Experimental results show that both mechanisms can provide satisfactory and purpose-aligned recommendation results, and users generally prefer the IDD mechanism, believing that it has higher flexibility, participation, and diversity. This study demonstrates the value of integrating dynamic user input into a lightweight, user-oriented design, enabling GRSs to better adapt to group needs and enhance the decision-making experience.
參考文獻 [1] M. Goyani, N. Chaurasiya, A Review of Movie Recommendation System: Limitations, Survey and Challenges, Electronic Letters on Computer Vision and Image Analysis 19 (2020) 18–37. https://doi.org/10.5565/rev/elcvia.1232. [2] M. van Setten, M. Veenstra, A. Nijholt, B. van Dijk, Goal-based structuring in recommender systems, Interact Comput 18 (2006) 432–456. https://doi.org/10.1016/j.intcom.2005.11.005. [3] G. Faggioli, M. Polato, F. Aiolli, Recency Aware Collaborative Filtering for Next Basket Recommendation, in: UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, Inc, 2020: pp. 80–87. https://doi.org/10.1145/3340631.3394850. [4] Y.L. Chen, L.C. Cheng, C.N. Chuang, A group recommendation system with consideration of interactions among group members, Expert Syst Appl 34 (2008) 2082–2090. https://doi.org/10.1016/j.eswa.2007.02.008. [5] G. Popescu, Group Recommender Systems as a Voting Problem, 2013. [6] J.A. Recio-García, L. Quijano, B. Díaz-Agudo, Including social factors in an argumentative model for Group Decision Support Systems, Decis Support Syst 56 (2013) 48–55. https://doi.org/10.1016/j.dss.2013.05.007. [7] H.J. Jeong, K.H. Lee, M.H. Kim, DGC: Dynamic group behavior modeling that utilizes context information for group recommendation, Knowl Based Syst 213 (2021). https://doi.org/10.1016/j.knosys.2020.106659. [8] L. Quijano-Sanchez, J.A. Recio-Garcia, B. Diaz-Agudo, G. Jimenez-Diaz, Social factors in group recommender systems, ACM Trans Intell Syst Technol 4 (2013). https://doi.org/10.1145/2414425.2414433. [9] H. Yin, Q. Wang, K. Zheng, Z. Li, J. Yang, X. Zhou, Social Influence-based Group Representation Learning for Group Recommendation, 2019. [10] K. Bok, J. Lim, H. Yang, J. Yoo, Social group recommendation based on dynamic profiles and collaborative filtering, Neurocomputing 209 (2016) 3–13. https://doi.org/10.1016/j.neucom.2015.11.120. [11] F. Barile, T. Draws, O. Inel, A. Rieger, S. Najafian, A. Ebrahimi Fard, R. Hada, N. Tintarev, Evaluating explainable social choice-based aggregation strategies for group recommendation, User Model User-Adapt Interact 34 (2024) 1–58. https://doi.org/10.1007/s11257-023-09363-0. [12] X. Wu, Y. Xiong, Y. Zhang, Y. Jiao, J. Zhang, Y. Zhu, P.S. Yu, ConsRec: Learning Consensus Behind Interactions for Group Recommendation, in: ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, Association for Computing Machinery, Inc, 2023: pp. 240–250. https://doi.org/10.1145/3543507.3583277. [13] Z. Huang, Y. Liu, C. Zhan, C. Lin, W. Cai, Y. Chen, A Novel Group Recommendation Model With Two-Stage Deep Learning, IEEE Trans Syst Man Cybern Syst 52 (2022) 5853–5864. https://doi.org/10.1109/TSMC.2021.3131349. 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Tanca, Recommending new items to ephemeral groups using contextual user influence, in: RecSys 2016 - Proceedings of the 10th ACM Conference on Recommender Systems, Association for Computing Machinery, Inc, 2016: pp. 285–292. https://doi.org/10.1145/2959100.2959137. [24] E. Ceh-Varela, H. Cao, H.W. Lauw, Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral Groups, ACM Trans Intell Syst Technol 13 (2022). https://doi.org/10.1145/3542804. [25] B. Kuechler, V. Vaishnavi, On theory development in design science research: anatomy of a research project, European Journal of Information Systems 17 (2008) 489–504. https://doi.org/10.1057/ejis.2008.40. [26] G. Adomavicius, B. Mobasher, F. Ricci, A. Tuzhilin, Context-aware recommender systems, AI Mag 32 (2011) 67–80. https://doi.org/10.1609/aimag.v32i3.2364. [27] U. Javed, K. Shaukat, I.A. Hameed, F. Iqbal, T.M. Alam, S. Luo, A Review of Content-Based and Context-Based Recommendation Systems, International Journal of Emerging Technologies in Learning 16 (2021) 274–306. https://doi.org/10.3991/ijet.v16i03.18851. [28] Q. Zhu, S. Wang, B. Cheng, Q. Sun, F. Yang, R.N. Chang, Context-aware group recommendation for point-of-interests, IEEE Access 6 (2018) 12129–12144. https://doi.org/10.1109/ACCESS.2018.2805701. [29] K. Haruna, M. Akmar Ismail, S. Suhendroyono, D. Damiasih, A. Pierewan, H. Chiroma, T. Herawan, Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction, Applied Sciences 7 (2017) 1211. https://doi.org/10.3390/app7121211. [30] K. Iizawa, N. Kobayashi, S. Shirasaka, What does the relationship between context and intent help for supporting design?, in: Proceedings - 2021 10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021, Institute of Electrical and Electronics Engineers Inc., 2021: pp. 606–611. https://doi.org/10.1109/IIAI-AAI53430.2021.00109. [31] Y.L. Lin, Y.X. Zheng, Y.C. Ku, Augmenting micro-moment recommendations with group and serendipity perspectives, Decis Support Syst 193 (2025). https://doi.org/10.1016/j.dss.2025.114454. [32] Y.L. Lin, S.W. Lee, A Personalized Interaction Mechanism Framework for Micro-moment Recommender Systems, ACM Trans Interact Intell Syst 13 (2023). https://doi.org/10.1145/3569586. [33] L. Lei, W. Dingding, L. Tao, K. Daniel, P. Balaji, SCENE : A Scalable Two-Stage Personalized News Recommendation System, Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2011) 1346. [34] J. Hron, K. Krauth, M.I. Jordan, N. Kilbertus, On component interactions in two-stage recommender systems, Adv Neural Inf Process Syst 34 (2021) 2744–2757. [35] K. Christakopoulou, A. Beutel, R. Li, S. Jain, E.H. Chi, Q&R: A two-stage approach toward interactive recommendation, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, 2018: pp. 139–147. https://doi.org/10.1145/3219819.3219894. [36] D. Jannach, A. Manzoor, W. Cai, L. Chen, A Survey on Conversational Recommender Systems, ACM Comput Surv 54 (2021). https://doi.org/10.1145/3453154. [37] L. Muchnik, S. Aral, S.J. Taylor, Social Influence Bias: A Randomized Experiment, Science (1979) 341 (2013) 647–651. https://doi.org/10.1126/science.1240466. [38] K. McCarthy, M. Salamó, L. Coyle, L. McGinty, B. Smyth, P. Nixon, Group recommender systems, in: Proceedings of the 11th International Conference on Intelligent User Interfaces, ACM, New York, NY, USA, 2006: pp. 267–269. https://doi.org/10.1145/1111449.1111506. [39] T. De Pessemier, S. Dooms, L. Martens, Comparison of group recommendation algorithms, Multimed Tools Appl 72 (2014) 2497–2541. https://doi.org/10.1007/s11042-013-1563-0. [40] K. Karmakar, A Context-Aware Approach To Restaurant Recommendations: System Algorithm and Case Study, (n.d.). https://doi.org/10.13140/RG.2.2.12720.38409. [41] I. Christensen, S. Schiaffino, M. Armentano, Social group recommendation in the tourism domain, J Intell Inf Syst 47 (2016) 209–231. https://doi.org/10.1007/s10844-016-0400-0. [42] M. Gartrell, X. Xing, Q. Lv, A. Beach, R. Han, S. Mishra, K. Seada, Enhancing group recommendation by incorporating social relationship interactions, in: Proceedings of the 16th ACM International Conference on Supporting Group Work - GROUP ’10, ACM Press, New York, New York, USA, 2010: p. 97. https://doi.org/10.1145/1880071.1880087. [43] S. Berkovsky, J. Freyne, Group-based recipe recommendations, in: Proceedings of the Fourth ACM Conference on Recommender Systems, ACM, New York, NY, USA, 2010: pp. 111–118. https://doi.org/10.1145/1864708.1864732. [44] X. Fan, Y. Ji, B. Hui, A dynamic preference recommendation model based on spatiotemporal knowledge graphs, Complex and Intelligent Systems 11 (2025). https://doi.org/10.1007/s40747-024-01658-y. [45] M. Zhang, S. Wu, X. Yu, Q. Liu, L. Wang, Dynamic Graph Neural Networks for Sequential Recommendation, IEEE Trans Knowl Data Eng 35 (2023) 4741–4753. https://doi.org/10.1109/TKDE.2022.3151618. [46] H. Xu, Y. Ding, J. Sun, K. Zhao, Y. Chen, Dynamic group recommendation based on the attention mechanism, Future Internet 11 (2019). https://doi.org/10.3390/fi11090198. [47] Q. Zhu, L. Wang, Context-Aware Restaurant Recommendation for Group of People, in: Proceedings - 2020 IEEE World Congress on Services, SERVICES 2020, Institute of Electrical and Electronics Engineers Inc., 2020: pp. 51–54. https://doi.org/10.1109/SERVICES48979.2020.00025. [48] L. Chen, M. Xia, A context-aware recommendation approach based on feature selection, Applied Intelligence 51 (2021) 865–875. https://doi.org/10.1007/s10489-020-01835-9. [49] J. Zeng, F. Li, H. Liu, J. Wen, S. Hirokawa, A restaurant recommender system based on user preference and location in mobile environment, in: Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Institute of Electrical and Electronics Engineers Inc., 2016: pp. 55–60. https://doi.org/10.1109/IIAI-AAI.2016.126. [50] D. Contreras, M. Salamó, L. 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描述 碩士
國立政治大學
資訊管理學系
112356006
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112356006
資料類型 thesis
dc.contributor.advisor 林怡伶zh_TW
dc.contributor.advisor Lin, Yi-Lingen_US
dc.contributor.author (Authors) 蔡凱亘zh_TW
dc.contributor.author (Authors) Tsai, Kai-Hsuanen_US
dc.creator (作者) 蔡凱亘zh_TW
dc.creator (作者) Tsai, Kai-Hsuanen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Sep-2025 15:03:44 (UTC+8)-
dc.date.available 1-Sep-2025 15:03:44 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2025 15:03:44 (UTC+8)-
dc.identifier (Other Identifiers) G0112356006en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159089-
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) 112356006zh_TW
dc.description.abstract (摘要) 團體推薦系統常常面臨資料稀少、偏好動態偏移以及難以達成團體共識等挑戰。本研究遵循設計科學的方法，提出了兩階段動態團體推薦系統，藉由兩階段的設計以及能夠捕捉使用者即時情境和意圖的動態機制，從而實現個人化且符合團體目的的團體推薦，且無需依賴團體的歷史資料。在第一階段，使用者根據當下的情境篩選器接收個人化推薦；在第二階段，這些推薦被聚合形成最終的團體推薦。本系統設計同時融合了兩種動態機制：由群組創建者擔任代表，基於領導者的推薦機制 (LBD) 和允許所有成員參與貢獻，基於個體的推薦機制 (IDD)。實驗結果表明，兩種機制均能提供令人滿意且目標一致的推薦結果，但使用者普遍更喜歡 IDD 機制，認為其具有更高的靈活性、參與度和多樣性。本研究證明了將動態的使用者輸入整合到輕量級、使用者導向的設計中，使團體推薦系統能夠更好地適應團體需求並增強決策體驗。zh_TW
dc.description.abstract (摘要) Group Recommendation Systems (GRSs) often face challenges such as data sparsity, dynamic preference shifts, and difficulty in reaching group consensus. Following the design science approach, this study proposes a two-stage dynamic GRS. Through a two-stage design and a dynamic mechanism that can capture users' immediate context and intent, it can achieve personalized and group-purpose group recommendations without relying on the historical group data. In the first stage, users receive individual recommendations based on current contextual filters; in the second stage, these are aggregated to form a final group recommendation. The system design incorporates two dynamic mechanisms: leader-based (LBD) where the group creator acts as the representative and sets the filter, and individual-driven (IDD) that allows all members to participate in expressing preferences. Experimental results show that both mechanisms can provide satisfactory and purpose-aligned recommendation results, and users generally prefer the IDD mechanism, believing that it has higher flexibility, participation, and diversity. This study demonstrates the value of integrating dynamic user input into a lightweight, user-oriented design, enabling GRSs to better adapt to group needs and enhance the decision-making experience.en_US
dc.description.tableofcontents 1. Introduction 5 2. Literature Review 10 2.1 Reaching Consensus in Group Recommendation 11 2.2 Emphasizing Dynamics in Recommendation Systems 12 2.3 Challenges in Data Sparsity 14 3. Research Framework and Development 17 3.1 Design Science Research Process and Design Principles 18 3.2 Preliminary Study 25 3.3 Recommendation Algorithm 27 4. Experiment Design 31 4.1 Dataset and Task 31 4.2 Participants and Pretest 32 4.3 Experiment Process 33 4.4 Measurement 35 5. Analysis and Results 37 5.1 Analysis of Onboarding Survey 37 5.2 Analysis of Field Study 38 5.3 Analysis of Post Survey 45 6. Discussion and Conclusion 52 6.1 Two-stage Approach and Dynamic Mechanism in GRS 53 6.2 Individual-Driven vs Leader-Based Dynamic Mechanism 53 6.3 Theoretical Implications 54 6.4 Practical Implications 55 6.5 Limitation and Future Work 57 Reference 58 Appendix 66zh_TW
dc.format.extent 2807983 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112356006en_US
dc.subject (關鍵詞) 團體推薦zh_TW
dc.subject (關鍵詞) 動態機制zh_TW
dc.subject (關鍵詞) 團體決策zh_TW
dc.subject (關鍵詞) Group Recommendationen_US
dc.subject (關鍵詞) Dynamic Mechanismen_US
dc.subject (關鍵詞) Group Decision-Makingen_US
dc.title (題名) 兩階段動態團體推薦系統：從個人到團體決策的動態適應機制zh_TW
dc.title (題名) A Two-Stage Dynamic Group Recommendation System: Dynamic Adaptation from Individual to Group Decision-Makingen_US
dc.type (資料類型) thesisen_US
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