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姓名 陳又暄(Yu-Hsuan Chen)  查詢紙本館藏   畢業系所 企業管理學系
論文名稱 以人格特質與質量擴散方法建立旅遊景點推薦模型
(Recommendation Model for Tourism by Personality Type Using Mass Diffusion Method)
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摘要(中) 推薦系統研究的主題遍布於各種領域,常見的如網路零售、電影、書籍……等。其中,旅遊相關的推薦系統也是廣為研究的主題之一。許多旅遊相關的推薦系統研究是使用協作過濾 (Collaborative Filtering) 方法,並試著在推薦系統方法加入人格特質以提升準確率。Zhou 等人 ( 2007) 認為質量擴散 (Mass Diffusion) 方法比協作過濾方法更加準確,不過此方法多是應用在推薦電影類型或是書籍等,較少應用在旅遊相關領域。針對質量擴散方法的研究,相比於其他的推薦系統方法,較少研究是考慮到人格特質,如大五人格或是 MBTI 16 型人格。本研究利用質量擴散方法建立旅遊景點推薦模型,並結合其他推薦系統研究中常用到的人格特質,大五人格和MBTI 16型人格,來達到旅遊景點個性化推薦。根據本研究實驗結果顯示,相比於協作過濾方法結合人格特質,質量擴散方法結合人格特質可以更精確地推薦景點給使用者。
摘要(英) Recommendation systems are studied in various fields, such as e-tailing, movies, books, ......, and so on. Among them, tourism recommendation systems are also one of the widely research topics. Many tourism recommendation system studies use Collaborative Filtering method and try to add personality traits to the recommendation system methods to improve the precision. Zhou et al. (2007) suggested that Mass Diffusion method has more precision than Collaborative Filtering method, but this method is mostly applied to recommending movie genres or books, but less often in tourism. Compared to other recommendation systems, fewer studies have taken into account personality traits such as Big Five Factor and MBTI 16 Personality Type. In this study, we used the Mass Diffusion method to establish a model of tourism attraction recommendation, and combined the personality traits commonly used in other recommendation system studies, such as Big Five Factor and MBTI 16 Personality Type, to achieve personalized recommendation of tourism attractions. According to the experimental results of this study, compared with the Collaborative Filtering method combined with personality traits, the Mass Diffusion method combined with personality traits can recommend attractions to users more accurately.
關鍵字(中) ★ 旅遊景點推薦
★ 大五人格
★ MBTI 16型人格
★ 質量擴散方法
關鍵字(英) ★ Tourism Recommendation
★ Big Five Factor
★ MBTI 16 Personality Type
★ Mass Diffusion Method
論文目次 中文摘要 i
ABSTRACT ii
目錄 iii
圖目錄 v
一、 緒論 1
1-1 研究背景 1
1-1-1旅遊業簡介 1
1-1-2旅遊推薦系統簡介 1
1-2 研究動機 3
1-3 研究目的 4
1-4 研究架構 4
二、 文獻回顧 5
2-1 質量擴散方法 5
2-2 人格特質 8
2-2-1 大五人格 8
2-2-2 MBTI 16型人格 10
三、 研究方法 11
3-1 研究流程 11
3-2 第一部分 — 質量擴散方法 12
3-3 第二部分 — 人格特質權重 14
3-4 結合人格特質與質量擴散方法 17
四、實驗 20
4-1 資料集 20
4-1-1旅遊景點選擇 20
4-1-2人格特質 20
4-2 資料處理 21
4-2-1大五人格計算方式 21
4-3 資料不平衡 23
4-4 資料集切分 25
4-5 推薦系統評估指標 26
4-5-1精確率 26
4-5-2召回率 27
4-6 實驗結果 28
五、 結論 33
5-1 研究結論 33
5-2 研究限制與未來研究建議 33
參考文獻 34
參考文獻 參考文獻

[1] Yiannakis, A., & Gibson, H. (1992). Roles tourists play. Annals of Tourism Research, 19(2), 287-303. doi: 10.1016/0160-7383(92)90082-z
[2] Braunhofer, M., Elahi, M., & Ricci, F. (2015). User Personality and the New User Problem in a Context-Aware Point of Interest Recommender System Information and Communication Technologies in Tourism 2015 (pp. 537-549).
[3] Crowel, H., Gribben, H., & Loo, J. (2014). Travel content takes off on YouTube. Think with Google.
[4] Neidhardt, J., Schuster, R., Seyfang, L., & Werthner, H. (2014). Eliciting the users′ unknown preferences. Paper presented at the Proceedings of the 8th ACM Conference on Recommender systems - RecSys ′14.
[5] Ishanka, U. P., & Yukawa, T. (2018). User Emotion and Personality in Context-aware Travel Destination Recommendation. Paper presented at the 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA).
[6] Hafshejani, Z. Y., Kaedi, M., & Fatemi, A. (2018). Improving sparsity and new user problems in collaborative filtering by clustering the personality factors. Electronic Commerce Research, 18(4), 813-836.
[7] Sertkan, M., Neidhardt, J., & Werthner, H. (2018). What is the “Personality” of a tourism destination? Information Technology & Tourism, 21(1), 105-133. doi: 10.1007/s40558-018-0135-6
[8] Al-Samarraie, H., Eldenfria, A., & Dawoud, H. (2017). The impact of personality traits on users’ information-seeking behavior. Information Processing & Management, 53(1), 237-247. doi: 10.1016/j.ipm.2016.08.004
[9] Jani, D. (2014). Relating travel personality to Big Five Factors of personality. Tourism: An International Interdisciplinary Journal, 62(4), 347-359.
[10] Tok, S. (2011). The big five personality traits and risky sport participation. Social Behavior and Personality: an international journal, 39(8), 1105-1111.
[11] Sahraoui Dhelim, Nyothiri Aung, Mohammed Amine Bouras, Huansheng Ning, & Cambria, E. (2021). A Survey on Personality-Aware Recommendation Systems.
[12] Zhou, T., Ren, J., Medo, M., & Zhang, Y. C. (2007). Bipartite network projection and personal recommendation. Phys Rev E Stat Nonlin Soft Matter Phys, 76(4 Pt 2), 046115. doi: 10.1103/PhysRevE.76.046115
[13] Zhang, F., Liu, Y., & Xiong, Q. (2017). A Novel Preferential Diffusion Recommendation Algorithm Based on User’s Nearest Neighbors. International Journal of Digital Multimedia Broadcasting, 2017, 1-7. doi: 10.1155/2017/1386461
[14] Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C. (2010). Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 107(10), 4511-4515.
[15] Bertani, R. M., A. C. Bianchi, R., & Costa, A. H. R. (2020). Combining novelty and popularity on personalised recommendations via user profile learning. Expert Systems with Applications, 146. doi: 10.1016/j.eswa.2019.113149
[16] Pervin, L. A. (1985). Personality: Current controversies, issues, and directions. Annual review of psychology, 36(1), 83-114.
[17] Costa, P. T., & McCrae, R. R. (1992). Normal personality assessment in clinical practice: The NEO Personality Inventory. Psychological assessment, 4(1), 5.
[18] Komarraju, M., Karau, S. J., Schmeck, R. R., & Avdic, A. (2011). The Big Five personality traits, learning styles, and academic achievement. Personality and individual differences, 51(4), 472-477.
[19] Gosling, S. D., Rentfrow, P. J., & Swann Jr, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in personality, 37(6), 504-528.
[20] Furnham, A. (2008). Personality and intelligence at work: Exploring and explaining individual differences at work.
[21] Yu, C. (2011). The Relationship between MBTI and Career Success-For Chinese Example. Paper presented at the 2011 International Conference on Management and Service Science.
[22] Garden, A. (1997). Relationships between MBTI profiles, motivation profiles, and career paths. Journal of Psychological type, 41, 3-16.
[23] McCaulley, M. H., & Martin, C. R. (1995). Career assessment and the Myers-Briggs type indicator. Journal of Career Assessment, 3(2), 219-239.
[24] Randall, K., Isaacson, M., & Ciro, C. (2017). Validity and reliability of the Myers-Briggs Personality Type Indicator: A systematic review and meta-analysis. Journal of Best Practices in Health Professions Diversity, 10(1), 1-27.
[25] Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and brain sciences, 24(1), 87-114.
[26] Lu, J. G., Liu, X. L., Liao, H., & Wang, L. (2020). Disentangling stereotypes from social reality: Astrological stereotypes and discrimination in China. Journal of personality and social psychology.
[27] Gosling, S., Rentfrow, P., & Potter, J. (2014). Norms for the ten item personality inventory. Unpublished data.
[28] Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.
[29] 朱郁筱, & 吕琳媛. (2012). 推荐系统评价指标综述.
[30] Zeng, W., Zeng, A., Shang, M.-S., & Zhang, Y.-C. (2013). Information filtering in sparse online systems: recommendation via semi-local diffusion. PLoS One, 8(11), e79354.
[31] Wang, Y., & Han, L. (2020). Personalized recommendation via network-based inference with time. Physica A: Statistical Mechanics and its Applications, 550, 123917.
[32] Jia, Z., Yang, Y., Gao, W., & Chen, X. (2015). User-based collaborative filtering for tourist attraction recommendations. Paper presented at the 2015 IEEE International Conference on Computational Intelligence & Communication Technology.
[33] Hu, R., & Pu, P. (2011). Enhancing collaborative filtering systems with personality information. Paper presented at the Proceedings of the fifth ACM conference on Recommender systems.
指導教授 許秉瑜 審核日期 2021-7-12
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