第五章、 結論與討論
5.4 未來研究建議
本研究總結生活型態及情境對於音樂偏好的影響，但目前關於生活型態的應 用只擴及部分領域，如前述的行銷及醫療，但在一般的推薦系統如電影、餐廳、
電視節目等領域的應用則只考慮情境因素，而尚未加入生活型態的探討，因此仍 有空間可望藉由生活型態因素來提升系統準確度。
此外，生活型態為一個廣泛的概念，所涵蓋的構面及因素非常多，本研究選 擇以時間維度來切割此概念，並僅探討其中的幾個構面，對於要真正藉由生活型 態來瞭解使用者偏好，還需要探討更多的因素，發掘這些因素與使用者偏好的關 連性，才能進一步使用這些因素作為預測使用者偏好的依據。
也可考慮將本研究延伸至更廣泛的使用者族群，包含不同職業及年齡層的使 用者，將有助於發掘更顯著的差異來區隔不同生活型態族群，並且在情境的考量 上加入團體的概念，考量聽音樂時非個人單獨聆聽的情況，如與朋友聚會、與家 人或與男/女朋友相處時，所偏好的音樂類型是否有差異，都值得深入的探討。
58
參考文獻
[1] 財團法人台灣網路資訊中心（民96年07月）。九十六年度台灣寬頻網路使用 狀況調查摘要分析。民97年02月19日，取自：
http://www.twnic.net.tw/download/200307/816a.pdf
[2] 資策會資訊市場情報中心（民95年12月12日）。2006台灣網路娛樂行為分析。
民97年02月19日，取自：
http://mic.iii.org.tw/intelligence/reports/pop_Doc_review.asp?docid=CDOC2006 1212005
[3] 林傑斌、林川雄、劉明德(民 93)。SPSS12 統計建模與應用實務。台北市：
博碩
[4] Abowd, G. D., & Mynatt, E. D. (2000). Charting past, present, and future research in ubiquitous computing. ACM Transactions on Computer-Human Interaction, 7(1), 29–58.
[5] Alspector, J., Kolcz, A., & Karunanithi, N. (1998). Comparing feature-based and clique-based user models for movie selection. Paper presented at the
proceedings of the Third ACM Conference on Digital Libraries, Pittsburgh, PA.
[6] Balabanovic, M., & Shoham, Y. (1997). Fabs: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
[7] Barthelmess, P., & Anderson, K. M. (2002). A view of software development environments based on activity theory, Computer Supported Cooperative Work, 11(1-2), 13–37.
[8] Berkovsky, S., Kuflik, T., & Ricci, F. (2005). Entertainment personalization mechanism through cross-domain user modeling. Intelligent Technologies for Interactive Entertainment, 3814, 215-219.
[9] Burke, R. (2002). Hybrid recommender systems: survey and experiments. User Modeling and User Adapted Interaction, 12(4), 331-370.
[10] Chan, J. K., & Leung, L. (2005). Lifestyles, reliance on traditional news media and online news adoption. New Media & Society, 7(3), 357–382.
[11] Chaney, D. (1996). Lifestyles. London: Routledge.
[12] Chen, H. C., & Chen, A. L. P. (2001, November). A music recommendation system based on music data grouping and user interests. Paper presented at the proceedings of the Tenth International Conference on Information and
Knowledge Management, Atlanta, Georgia.
[13] Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M.
(1999). Combining content-based and collaborative filters in an online newspaper. Paper presented at the proceedings of ACM SIGIR Workshop on
59
Recommender Systems, California.
[14] Collins, P., Shukla, S., & Redmiles, D. (2002). Activity theory and system design: a view from the trenches. Computer Supported Cooperative Work, 11(1-2), 55–80.
[15] Coyle, L., Balfe, E., Stevenson, G., Neely, S., Dobson, S., Nixon, P., Smyth, B.
(2006). Supplementing case-based recommenders with context data. Paper presented at the proceedings of the 1st International Workshop on Case Based Reasoning and Context-Awareness, Turkey.
[16] Crawford, K., & Hasan, H. (2006). Demonstrations of the activity theory framework for research in IS. Australasian Journal of Information Systems, 13(2), 49-68.
[17] Daghfous, N., Petrof, J. V., & Pons, F. (1999). Values and adoption of
innovations: a cross-cultural study. Journal of Consumer Marketing, 16(4), 314-331.
[18] Engeström, Y., Miettinen, R., & Punamäki, R. L. (1999). Perspectives on Activity Theory. Cambridge: Cambridge University Press.
[19] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70
[20] Guan, D., Li, Q., Lee, S., & Lee, Y. (2006). A context-aware music
recommendation agent in smart office. Fuzzy Systems and Knowledge Discovery, 4223, 1201-1204.
[21] Hawkins, D. I., Mothersbaugh, D. L., & Best, R. J. (2007). Consumer behavior:
building marketing strategy. (10th ed.). Boston :McGraw-Hill/Irwin.
[22] Hayes, C., Cunningham, P., Clerkin, P., & Grimaldi, M. (2002).
Programme-driven music radio. Paper presented at the proceedings of the 15th European Conference on Artificial Intelligence, Lyon, France.
[23] Honkanen, P., Olsen, S. O., & Myrland, Ø. (2004). Preference-based
segmentation: a study of meal preferences among Norwegian teenagers. Journal of Consumer Behaviour, 3(3), 235–250.
[24] Huang, L. P. (2005). A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert System with Applications, 29, 383-392.
[25] Kaenampornpan, M., & O’Neill, E. (2004). An integrated context model:
bringing activity to context. Paper presented at the proceedings of UbiComp 2004 Workshop on Advanced Context Modelling, Reasoning and Management, Nottingham, England.
[26] Kaynak, E., & Kara, A. (2001). An examination of the relationship among consumer lifestyles, ethnocentrism, knowledge structures, attitudes and
60
behavioural tendencies: a comparative study in two CIS states. International Journal of Advertising, 20(4), 457–482.
[27] Kofod-Petersen, A., & Cassens, J. (2006). Using activity theory to model context awareness. Modeling and Retrieval of Context, 3946, 1-17.
[28] Kuo, F.F., Chiang, M. F., Shan, M. K., & Lee, S. Y. (2005). Emotion-based music recommendation by association discovery from film music. Paper
presented at the proceedings of the 13th annual ACM international conference on Multimedia, Hilton, Singapore.
[29] Kuo, F. F., & Shan, M. K. (2002). A personalized music filtering system based on melody style classification. Paper presented at the proceedings of Second IEEE international Conference on Data Mining, Maebashi, Japan.
[30] Kuutti, K. (1995). Activity theory as a potential framework for humancomputer interaction research. In B. A. Nardi (Eds.), Context and Consciousness: Activity Theory and Human Computer Interaction (pp. 17-44). Cambridge, MA: MIT Press.
[31] Lekakos, G., & Giaglis, G. M. (2004). A lifestyle-based approach for delivering personalized advertisements in digital interactive television. Journal of Computer Mediated Communication, 9(2).
[32] Li, Q., Kim, B. M., Guan, D. H., & Oh, D. W. (2004). A music recommender based on audio features. Paper presented at the proceedings of the 27th annual international ACM SIGIR conference on Research and development in
information retrieval, Sheffield, UK.
[33] Linden, G., Smith, B., and York, J. (2003). Amazon.com recommendations: item to item collaborative filtering. IEEE Internet Computing, 7(1), 76–80.
[34] Matzler, K., Hattenberger, G., Pechlaner, H., & Abfalter, D. (2004). Lifestyle segmentation, vacation types and guest satisfaction. In C. Cooper, C. Arcodia, D.
Solnet, & M. Whitford (Eds.), Creating tourism knowledge: a selection of papers from CAUTHE 2004 (pp.127-137). Australia: Common Ground Publishing Pty Ltd.
[35] Melville, P., Mooney, R., & Nagarajan, R. (2002, July). Content-boosted collaborative filtering for improved recommendations. Paper presented at the proceedings of the Eighteenth National Conference on Artificial Intelligence, Edmonton, Alberta, Canada.
[36] North, A. C., & Hargreaves, D. J. (2007a). Lifestyle correlates of musical preference: 1. relationships, living arrangements, beliefs, and crime. Psychology of Music, 35, 58-87.
[37] North, A. C., & Hargreaves, D. J. (2007b). Lifestyle correlates of musical
preference: 2. media, leisure time, and music. Psychology of Music, 35, 179-200.
61
[38] North, A. C., & Hargreaves, D. J. (2007c). Lifestyle correlates of musical preference: 3. travel, money, education, employment, and health. Psychology of Music, 35, 473-497.
[39] North, A. C., Hargreaves, D. J., & Hargreaves, J. J. (2004). Uses of music in everyday life. Music Perception, 22(1), 41-77.
[40] Park, H., & Lee, J. (2005). A framework of context-awareness for ubiquitous computing middlewares. Paper presented at the proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science, Jeju Island, South Korea.
[41] Park, H. S., Yoo, J. O., & Cho, S. B. (2006). A context-aware music
recommendation system using fuzzy bayesian networks with utility theory. Fuzzy Systems and Knowledge Discovery, 4223, 970-979.
[42] Pearson, J. L., & Dollinger, S. J. (2004). Music preference correlates of jungian types. Personality and Individual Differences, 36, 1005–1008.
[43] Perugini, S., Gonçalves, M. A., & Fox, E. A. (2004). Recommender systems research: a connection-centric survey. Journal of Intelligent Information Systems, 23(2), 107–143.
[44] Plummer, J. T. (1974). The concept and application of life style segmentation.
Journal of Marketing, 38(1), 33-37.
[45] Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi’s of everyday life: the structure and personality. Journal of Personality and Social Psychology, 84(6), 1236–1256.
[46] Riecken, D. (2000). Personalized views of personalization. Communications of the ACM, 43(8), 27–28.
[47] Sato, K. (2004). Context-sensitive approach for interactive systems design:
modular scenario-based methods for context representation. Journal of Physiological Anthropology and Applied Human Science, 23(6), 277–281.
[48] Schuessler, K. F. (1948). Social background and musical taste. American Sociological Review, 13(3), 330-335.
[49] Schwartz, K. D., & Fouts, G. T. (2003). Music preferences, personality style, and developmental issues of adolescents. Journal of Youth and Adolescence, 32(3), 205–213.
[50] Shardanand, U., & Maes, P. (1995). Social information filtering: algorithms for automating “word of mouth.” In I. R. Katz, R. L. Mack, L. Marks, M. B. Rosson,
& J. Nielsen (Eds.), CHI 95: Human Factors in Computing Systems (pp.210-217). Denver, Colorado: ACM Press.
[51] Smyth, B., Cotter, P. (2000). A personalised TV listings service for the digital TV age. Knowledge-Based Systems, 13, 53-59.
62
[52] Tai, S. H. C., & Tam, J. L. M. (1997). A lifestyle analysisof female consumersin greater china. Psychology & Marketing, 14(3), 287–307.
[53] Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals.
IEEE Transactions on Speech and Audio Processing, 10(5), 293–302.
[54] Uitdenbogerd, A., & Schyndel, R. V. (2002, October). A review of factors affecting music recommender success. Paper presented at the proceedings of the 3rd International Conference on Music Information Retrieval, Paris.
[55] Urbonavičius, S., & Kasnauskienė, G. (2005). New applications of a traditional psychographic segmentation concept. Engineering Economics, 5(45), 80-86.
[56] Vyncke, P. (2002). Lifestyle segmentation: from attitudes, interests and opinions, to values, aesthetic styles, life visions and media preferences. European Journal of Communication, 17(4), 445–463.
[57] Wang, J., Pouwelse, J., Fokker, J., V, A. P. & Reinders M. J. T. (2008).
Personalization on a peer-to-peer television system. Multimedia Tools and Applications, 36(1-2), 89-113.
[58] Weiser, M. (1993). Hot topic: ubiquitous computing. IEEE Computer, 71-72.
[59] Yang, K. C. C. (2004). A comparison of attitudes towards Internet advertising among lifestyle segments in Taiwan. Journal of Marketing Communications, 10, 195–212.
[60] Yapriady, B., & Uitdenbogerd, A. L. (2005). Combining demographic data with collaboration filtering for automatic music recommendation. Knowledge-Based Intelligent Information and Engineering Systems, 3684, 201-207.
63
64
65
66
67
Alternative Rock 另類搖滾 Blues 藍調
68
在這個情境下，我喜歡聽什麼樣的音樂？
情境一 忙了一天終於回到家裡，覺得很疲倦，終於有時間可以上網看文章、
或跟朋友開心的聊天，這時想聽點音樂來放鬆、讓心靈休息。
作答區 最想聽的音樂是： 請點選
第二想聽的音樂是： 請點選，若無可不必點選 第三想聽的音樂是： 請點選，若無可不必點選
情境二 正在回家的火車/客運上面，因為要回家所以心情很不錯，四周都有人 在說話、很嘈雜，這時想聽點音樂，可能是特別想到某個歌手或是享 受在音樂營造的氣氛中。
作答區 最想聽的音樂是： 請點選
第二想聽的音樂是： 請點選，若無可不必點選 第三想聽的音樂是： 請點選，若無可不必點選
情境三 你正在公司/學校，手邊有需要專心思考的工作待處理，其他人雖然也 在作自己的事情，但偶而會有聊天或說話聲，這時想聽點音樂幫助自 己更專心、並且提神以免睡著。
作答區 最想聽的音樂是： 請點選
第二想聽的音樂是： 請點選，若無可不必點選 第三想聽的音樂是： 請點選，若無可不必點選
第三部份、基本資料
以下的問題將詢問您的基本資料，請您就實際情形，在作答區內填入符合的 數值。
出生年份：民國請點選年 性 別：請點選
職 業：請點選 家 境：請點選
每週聽音樂頻率：請點選