政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146885
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 113392/144379 (79%)
造訪人次 : 51219576      線上人數 : 913
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/146885


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/146885


    題名: 探討情緒對偶然驚喜推薦系統的設計與影響
    The design and influence of emotion on serendipity recommender system
    作者: 郭蕎銥
    Guo, Ciao-Yi
    貢獻者: 林怡伶
    Lin, Yi-Ling
    郭蕎銥
    Guo, Ciao-Yi
    關鍵詞: 情緒
    偶然驚喜推薦系統
    好奇心
    使用者偏好
    情緒識別
    Emotion
    Serendipity recommender system
    Curiosity
    User preference
    Emotion Recognition
    日期: 2023
    上傳時間: 2023-09-01 14:52:45 (UTC+8)
    摘要: 傳統推薦系統大多只追求推薦的準確性，根據使用者的歷史行為和偏好，推薦其相關物品，這樣的推薦雖然能減輕資訊過量的問題，幫助使用者做出合適的決定。然而，卻導致過度專業化，讓用戶覺得缺乏新鮮感，對系統的推薦失去興趣。根據過去研究，在系統引入偶然驚喜能夠有效解決過度專業化的問題並提高滿意。為了解決這個問題，推薦系統可以引入偶然驚喜的推薦機制。好奇心是促使人們探索行為的重要因素，促進人們對偶然驚喜的探索。現行的偶然驚喜推薦系統多基於使用者的好奇心，推薦可能出乎使用者意料、但又符合使用者興趣和偏好的物品。除了個性外，情緒也會影響人的心情，而心情會影響人的決策。情緒會隨著時間變化，被視為是使用者短期偏好的相關因素，且會影響使用者對偶然驚喜的想法跟接受度。然而，以往的偶然驚喜推薦系統很少考慮用戶的情緒。本研究透過提供使用者不同偶然驚喜程度的推薦列表，探討情緒是否影響使用者對偶然驚喜推薦策略的接受傾向，了解情緒與使用者對偶然驚喜推薦偏好的關係。研究結果指出，除了好奇心外，情緒也會影響使用者對偶然驚喜推薦策略的偏好與接受傾向。因此，未來的偶然驚喜推薦系統，除了基於好奇心，也可以納入使用者的情緒，去決定推薦的偶然驚喜程度，以提升使用者對推薦的滿意度。
    Recommender systems can eliminate users’ information overload and help users make proper decisions by suggesting items based on users’ preferences. However, most current recommender systems overemphasize accuracy. That might cause an overspecialization problem and even lose users’ interest. To overcome the problem, the recommender system can suggest serendipitous items. Exploratory behavior is a facilitator of serendipity. Curiosity, a personality trait, is the most considered characteristics for people’s explorative behaviors and serendipity recommender system. Other than personality, mood is also affected by emotion and influences people’s decision-making. Emotion changes over time, which can be regarded as a relevant factor to short-term user preference and influence users’ thoughts and behavior toward serendipitous information. However, previous serendipity recommender system rarely takes users’ emotion into account. In this research, we proposed serendipity recommendation with different serendipity level to discuss whether emotion matter for the serendipity recommender system and know the relationship between emotion and users’ serendipity preference toward serendipity recommendation list. The result shows that users’ emotion has significant influence on their serendipity preference. Therefore, we believe that incorporating user’s emotion into future serendipity recommendations would improve users’ satisfaction.
    參考文獻: Abbas, F., & Niu, X. (2019). One size does not fit all: Modeling users’ personal curiosity in recommender systems. ArXivorg.
    Abbas, R., Hassan, G. M., Al-Razgan, M., Zhang, M., Amran, G. A., Al Bakhrani, A. A., Alfakih, T., Al-Sanabani, H., & Rahman, S. M. M. (2022). A serendipity-oriented personalized trip recommendation model. Electronics, 11(10), 1660.
    Abdul, A., Chen, J., Liao, H.-Y., & Chang, S.-H. (2018). An emotion-aware personalized music recommendation system using a convolutional neural networks approach. Applied Sciences, 8(7), 1103.
    Adamopoulos, P., & Tuzhilin, A. (2014). On unexpectedness in recommender systems: Or how to better expect the unexpected. ACM Transactions on Intelligent Systems and Technology (TIST), 5(4), 1–32.
    Alrihaili, A., Alsaedi, A., Albalawi, K., & Syed, L. (2019). Music recommender system for users based on emotion detection through facial features. In 2019 12th International Conference on Developments in eSystems Engineering (DeSE). IEEE.
    Altan, A., & Karasu, S. (2019). The effect of kernel values in support vector machine to forecasting performance of financial time series. The Journal of Cognitive Systems, 4, 17–21.
    Armenta, C. N., Fritz, M. M., & Lyubomirsky, S. (2017). Functions of positive emotions: Gratitude as a motivator of self-improvement and positive change. Emotion Review, 9(3), 183–190.
    Bechara, A. (2003). Risky business: emotion, decision-making, and addiction. Journal of Gambling Studies, 19(1), 23–51.
    Beedie, C., Terry, P., & Lane, A. (2005). Distinctions between emotion and mood. Cognition & Emotion, 19(6), 847–878.
    Beharrell, B., & Denison, T. J. (1995). Involvement in a routine food shopping context. British Food Journal, 97(4), 24–29.
    Björneborn, L. (2017). Three key affordances for serendipity: Toward a framework connecting environmental and personal factors in serendipitous encounters. Journal of Documentation.
    Bloch, P. H., & Richins, M. L. (1983). A theoretical model for the study of product importance perceptions. Journal of Marketing, 47(3), 69–81.
    Cai, C. X., & Chau, P. Y. (2016). Motivating identity-related behaviors in online community--a broaden-and-build perspective.
    Chen, J., & You, F. (2020). Text Summarization Generation Based on Semantic Similarity. In 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) (pp. 946–949). IEEE.
    Chen, L., Yang, Y., Wang, N., Yang, K., & Yuan, Q. (2019). How serendipity improves user satisfaction with recommendations? a large-scale user evaluation. In The world wide web conference (pp. 240–250).
    Conway, A. M., Tugade, M. M., Catalino, L. I., & Fredrickson, B. L. (2013). The broaden-and-build theory of positive emotions: Form, function and mechanisms. The Oxford Handbook of Happiness, 17–34.
    Denovan, A., & Macaskill, A. (2017). Stress, resilience and leisure coping among university students: applying the broaden-and-build theory. Leisure Studies, 36(6), 852–865.
    Forsyth, K. (2016). Testing the broaden-and-build theory in early adolescence: exploring associations of positive affect and problem solving coping strategies. University of British Columbia.
    Fredrickson, B. L. (1998). What good are positive emotions? Review of General Psychology, 2(3), 300–319.
    Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56(3), 218.
    Fredrickson, B. L. (2004). The broaden--and--build theory of positive emotions. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 359(1449), 1367–1377.
    Fredrickson, B. L. (2013). Positive emotions broaden and build. In Advances in experimental social psychology (Vol. 47, pp. 1–53). Elsevier.
    Fredrickson, B. L., & Cohn, M. A. (2008). Positive emotions.
    Fredrickson, B. L., & Joiner, T. (2002). Positive emotions trigger upward spirals toward emotional well-being. Psychological Science, 13(2), 172–175.
    Garcia-Garcia, J. M., Penichet, V. M., Lozano, M. D., Garrido, J. E., & Law, E. L.-C. (2018). Multimodal affective computing to enhance the user experience of educational software applications. Mobile Information Systems, 2018.
    Gross, J. J., Richards, J. M., & John, O. P. (2006). Emotion regulation in everyday life.
    Grossnickle, E. M. (2016). Disentangling curiosity: Dimensionality, definitions, and distinctions from interest in educational contexts. Educational Psychology Review, 28(1), 23–60.
    Gupta, A., Eilert, M., & Gentry, J. W. (2018). Can I surprise myself? A conceptual framework of surprise self-gifting among consumers. Journal of Retailing and Consumer Services, 54, 101712.
    Ho, A. T., Menezes, I. L., & Tagmouti, Y. (2006). E-mrs: Emotion-based movie recommender system (pp. 1–8).
    Houston, M. J. (1978). Conceptual and methodological perspectives on involvement. Research Frontiers in Marketing: Dialogues and Directions, 184–187.
    Howard, J. A., & Sheth, J. N. (1969). The theory of buyer behavior. New York, 63, 145.
    Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G., & Molino, P. (2010). Can a recommender system induce serendipitous encounters. E-Commerce, 1–17.
    Isen, A. M. (1990). The influence of positive and negative affect on cognitive organization: Some implications for development. Psychological and Biological Approaches to Emotion, 75–94.
    Kaminskas, M., & Bridge, D. (2014). Measuring surprise in recommender systems. In Proceedings of the workshop on recommender systems evaluation: dimensions and design (Workshop programme of the 8th ACM conference on recommender systems). Citeseer.
    Kaminskas, M., & Ricci, F. (2012). Contextual music information retrieval and recommendation: State of the art and challenges. Computer Science Review, 6(2–3), 89–119.
    Kapoor, K., Kumar, V., Terveen, L., Konstan, J. A., & Schrater, P. (2015). “ I like to explore sometimes” Adapting to Dynamic User Novelty Preferences. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 19–26).
    Kashdan, T. B., Gallagher, M. W., Silvia, P. J., Winterstein, B. P., Breen, W. E., Terhar, D., & Steger, M. F. (2009a). The curiosity and exploration inventory-II: Development, factor structure.
    Kashdan, T. B., Gallagher, M. W., Silvia, P. J., Winterstein, B. P., Breen, W. E., Terhar, D., & Steger, M. F. (2009b). The curiosity and exploration inventory-II: Development, factor structure, and psychometrics. Journal of Research in Personality, 43(6), 987–998.
    Kashdan, T. B., Rose, P., & Fincham, F. D. (2004). Curiosity and exploration: Facilitating positive subjective experiences and personal growth opportunities. Journal of Personality Assessment, 82(3), 291–305.
    Kim, H.-D., & Sim, K.-B. (2007). Emotion Recognition Method for Driver Services. International Journal of Fuzzy Logic and Intelligent Systems, 7(4), 256–261.
    Kim, H.-S. (2005). Consumer profiles of apparel product involvement and values. Journal of Fashion Marketing and Management: An International Journal, 9(2), 207–220.
    Kotkov, D., Veijalainen, J., & Wang, S. (2016). Challenges of serendipity in recommender systems. In International conference on web information systems and technologies. SCITEPRESS.
    Kotkov, D., Wang, S., & Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowledge-Based Systems, 111, 180–192.
    Kshirsagar, S. (2002). A multilayer personality model. In Proceedings of the 2nd international symposium on Smart graphics (pp. 107–115).
    Kuppens, P., & Verduyn, P. (2017). Emotion dynamics. Current Opinion in Psychology, 17, 22–26.
    Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making. Annual Review of Psychology, 66(1).
    Ley, M., Egger, M., & Hanke, S. (2019). Evaluating Methods for Emotion Recognition based on Facial and Vocal Features. In AmI (Workshops/Posters) (pp. 84–93).
    Logesh, R., & Subramaniyaswamy, V. (2019). Exploring hybrid recommender systems for personalized travel applications. In Cognitive informatics and soft computing (pp. 535–544). Springer.
    M. Rusdi, Z., & Wibowo, A. (2022). Team mindfulness, team commitment and team respectful engagement: the lens of the conservation of resources theory and the broaden-and-build theory. Organization Management Journal, 19(5), 189–199.
    Maccatrozzo, V., Terstall, M., Aroyo, L., & Schreiber, G. (2017). SIRUP: Serendipity in recommendations via user perceptions. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (pp. 35–44).
    Maksai, A., Garcin, F., & Faltings, B. (2015). Predicting online performance of news recommender systems through richer evaluation metrics. In Proceedings of the 9th ACM Conference on Recommender Systems (pp. 179–186).
    McShane, S., & Von Glinow, M. (2011). M: Organizational behavior. Irwin/McGraw-Hill.
    Menk, A., Sebastia, L., & Ferreira, R. (2017). Curumim: A serendipitous recommender system for tourism based on human curiosity. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 788–795). IEEE.
    Mouakhar-Klouz, D., d’Astous, A., & Darpy, D. (2016). I’m worth it or I need it? Self-gift giving and consumers’ self-regulatory mindset. Journal of Consumer Marketing, 33(6), 447–457.
    Polignano, M., Narducci, F., de Gemmis, M., & Semeraro, G. (2021). Towards emotion-aware recommender systems: an affective coherence model based on emotion-driven behaviors. Expert Systems with Applications, 170, 114382.
    Poria, S., Cambria, E., Bajpai, R., & Hussain, A. (2017). A review of affective computing: From unimodal analysis to multimodal fusion. Information Fusion, 37, 98–125.
    Pu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems (pp. 157–164).
    Rahimi, A., & Bigdeli, R. A. (2014). The broaden-and-build theory of positive emotions in second language learning. Procedia-Social and Behavioral Sciences, 159, 795–801.
    Rochon, J. (1998). Application of GEE procedures for sample size calculations in repeated measures experiments. Statistics in Medicine, 17(14), 1643–1658.
    Rust, R. T., & Oliver, R. L. (2000). Should we delight the customer? Journal of the Academy of Marketing Science, 28, 86–94.
    Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X. (2018). A review of emotion recognition using physiological signals. Sensors, 18(7), 2074.
    Solis, B. (2015, August 24). Best Practices: 10 Ways Marketers Can Compete for Micro-Moments: The New Moment of Truth: Google’s Micro-Moments Reshape the Marketing Funnel. http://adage.com/article/digitalnext/practices-cmos-advantage-micro-moments/298855/
    Sridharan, S. (2014). Introducing serendipity in recommender systems through collaborative methods.
    Stitini, O., Kaloun, S., & Bencharef, O. (2022). An improved recommender system solution to mitigate the over-specialization problem using genetic algorithms. Electronics, 11(2), 242.
    Sun, X., Zhou, X., Wang, Q., & Sharples, S. (2022). Investigating the impact of emotions on perceiving serendipitous information encountering. Journal of the Association for Information Science and Technology, 73(1), 3–18.
    Swann, W. B., Stein-Seroussi, A., & McNulty, S. E. (1992). Outcasts in a white-lie society: The enigmatic worlds of people with negative self-conceptions. Journal of Personality and Social Psychology, 62(4), 618.
    Tan, B.-R. (2021). 基於影像, 聲音及手機感測資料融合的多模情緒辨識系統.
    Taramigkou, M., Apostolou, D., & Mentzas, G. (2017). Supporting creativity through the interactive exploratory search paradigm. International Journal of Human--Computer Interaction, 33(2), 94–114.
    Tkalcic, M., Semeraro, G., & de Gemmis, M. (2014). Personality and Emotions in Decision Making and Recommender Systems. In DMRS (pp. 14–18).
    Wang, N., & Chen, L. (2023). How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysis. User Modeling and User-Adapted Interaction, 33(3), 727–765.
    Wang, N., Chen, L., & Yang, Y. (2020). The Impacts of Item Features and User Characteristics on Users’ Perceived Serendipity of Recommendations. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 266–274).
    Yao, Q. (2016). Multi-sensory emotion recognition with speech and facial expression.
    Zaichkowsky, J. L. (1985). Measuring the involvement construct. Journal of Consumer Research, 12(3), 341–352.
    Zhang, M., Yang, Y., Abbas, R., Deng, K., Li, J., & Zhang, B. (2021). SNPR: A Serendipity-Oriented Next POI Recommendation Model. In Proceedings of the 30th ACM International Conference on Information \\& Knowledge Management (pp. 2568–2577).
    Zhang, Y. C., Séaghdha, D. Ó., Quercia, D., & Jambor, T. (2012). Auralist: introducing serendipity into music recommendation. In Proceedings of the fifth ACM international conference on Web search and data mining (pp. 13–22).
    Zhao, S., Jia, G., Yang, J., Ding, G., & Keutzer, K. (2021). Emotion recognition from multiple modalities: Fundamentals and methodologies. IEEE Signal Processing Magazine, 38(6), 59–73.
    Zheng, Y., Mobasher, B., & Burke, R. D. (2013). The Role of Emotions in Context-aware Recommendation. Decisions@ RecSys, 2013, 21–28.
    Ziarani, R. J., & Ravanmehr, R. (2021). Serendipity in recommender systems: a systematic literature revie. 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
    110356015
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110356015
    資料類型: thesis
    顯示於類別:[資訊管理學系] 學位論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    601501.pdf2222KbAdobe PDF20檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    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 ©   - 回饋