• 沒有找到結果。

6.1 結論

本研究針對史前館之模擬參觀結果顯示,關聯規則所挖掘出的不只有順向的 規則,也有逆向的規則,而本研究按照史前館方的建議參觀方向進行推薦時,其 成效較隨機佳。在各展廳的推薦次數中,按照關聯規則所建議之路徑結果,在自 然史展區中,冰期展廳推薦下一個新生代展廳雖然以常識來是理所當然的,但從 抽樣結果上卻顯示不一定,參觀冰期展廳的觀眾不一定喜歡參觀新生代展廳。

從人類的文化展聽到臺灣史前文化生活展廳之規則在關聯規則中是有趣的 結果,因為人類的文化展廳是以通道的方式作展覽,而館方建議參觀路徑是從人 類的文化展廳至臺灣史前序幕展廳,故應該於參觀時先行參觀臺灣史前序幕展廳 再參觀臺灣史前人的生活展廳,但關聯規則顯示的結果是觀眾可能直接選擇到臺 灣史前人的生活展廳中參觀。

在史前文化展區中,大部分觀眾會集中於臺灣史前文化生活、臺灣史前陶器、

臺灣史前人與海洋、卑南遺址的卑南文化、巨石與祭祀等展廳中,就算是逆向的 參觀史前文化展區,也會集中這幾各展廳中,而產生的規則中較為有趣的規則是 看完史前人的生活較長的觀眾在卑南遺址的卑南文化也停留較長的時間。

在推薦引擎中納入觀眾的停留時間可以提供較為個人化的結果,根據不同的 偏好,所選擇停留時間較長的展廳也有所不同。起初推薦系統機制無法進行計算 時,藉由關聯規則所產生的分數能夠在第一次參觀停留時間較高展廳時就進行推 薦,而推薦系統在第二次停留時間較長時會開始進行計算,由於會不斷藉由過去 的資料進行加權計算,故推薦結果會不斷改變。在推薦的評估中顯示推薦次數在 6 次時成功的機率最高,有 77%的機率可以推薦成功。

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6.2 討論

本研究推薦系統應用於史前館中可能出現以下幾種問題待解決。

第一:現在由於只發展到展廳推薦的階段,尚無展品推薦,觀眾需要更進一 步的推薦及引導,展廳推薦雖然可提供大方向的參觀路徑,但是需要更近一步的 資訊時還是需要收集更多的資料來提升預測精準度,然而展品推薦是否能夠使用 現有的推薦模式尚需進一步的研究。

第二:而針對觀眾參觀行為,可針對不同年齡層或不同參觀目的建立推薦項 目。亦可應用其他資料採礦方法來建立觀眾的決策模式做為推薦依據。如果要做 更為精準的分析則需要更多的參觀資料或背景資料。

第三:本研究以關聯規則來挖掘出隱藏的路徑,但針對路徑問題並非只有一 種計算方法,故可針對路徑問題做進一步的研究。本研究中只使用一種方向作為 推薦路徑,在史前館中由於設計問題,展廳分布在二樓與地下一樓中,並且單樓 層展廳是以環型的方式設計,有可能會出現繞圈的情況,此種情況是否會對觀眾 造成影響需要近一步的研究,另外由於在環形的設計中,會出現正逆向參觀的路 徑,故可針對常設展廳的路徑進行調整。

第四:由於資料轉換上只有較長或較短之選擇,在本研究中受到樣本數量限 制,無法切割成更多層的分別,若能切割為更多層,則能做更精細的計算。

本研究有幾項研究限制,第一,本研究使用的資料為史前館 98、100 年展廳 停留時間資料進行模擬計算。第二,本次測量的僅有史前館常設展廳,其他特展 及園區尚無加入計算中。第三,由於關聯規則尚無使用即時計算的方式,故無法 呈現完全動態的計算結果。

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Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems a survey of the state-of-the-art and possible extensions. IEEE

Transactions on Knowledge and Data Engineering, pp. 734 - 749.

Adomavicius, G., & Tuzhilin, A. (2011). Context-Aware Recommender Systems.

Springer US.

Agrawal, R., & Srikant, R. (1994). Fast Algorithms for Mining Association Rules.

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases ,pp. 487-499. Morgan Kaufmann Publishers Inc.

Alabastro, P., Ang, M., deGuzman, R., Muhi, M., & Suarez, M. (2010). MyMuseum:

Integrating personalized recommendation and multimedia for enriched human-system interaction. Digital Content, Multimedia Technology and its Applications (IDC), 2010 6th International Conference on , pp. 421 - 426.

Seoul: IEEE.

Amatriain, X., Jaimes, A., Oliver, N., & Pujol, J. M. (2011). Data Mining Methods for Recommender Systems. Springer US.

48

Balabanović, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM pp. 66-72. ACM.

Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, pp. 109-132.

Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Morgan Kaufmann Publishers.

Burke, R. (2000). Knowledge-based recommender systems. Encyclopedia of Library and Information Systems, pp. 180-200.

Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, pp. 331-370.

Chen, C.-C., & Huang, T.-C. (2012). Learning in a u-Museum: Developing a context-aware ubiquitous learning. Computers & Education, pp. 873-883.

Farhoomand, A., & Drury, D. (2002). Managerial information overload.

Communications of the ACM (pp. 127-131). ACM New York, NY, USA.

Huang, Y.-M., Liu, C.-H., Lee, C.-Y., & Huang, Y.-M. (2012, 10). Designing a Personalized Guide Recommendation System to Mitigate Information

Overload in Museum Learning. Journal of Educational Technology & Society, pp. 150-166.

Lin, W. (2000). Association Rule Mining for Collaborative Recommender Systems.

Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, pp. 19-21.

Lü, L., Medo, M., Yeung, C., Zhang, Y.-C., Zhang, Z.-K., & Zhou, T. (2012).

Recommender systems. Physics Reports, pp. 1-49.

Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012, 9 1). A literature review and classification of recommender systems research. Expert Systems with

Applications: An International Journal, pp. 10059-10072.

Pazzani, M. J. (1999). A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review , pp. 393-408.

Pazzani, M. J., & Billsus, D. (2007). Content-Based Recommendation Systems. The Adaptive Web, pp. 325-341.

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007, 12 1). A Design Science Research Methodology for Information Systems Research.

Journal of management information systems, pp. 45-77.

Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM (pp. 56-58). ACM.

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens:

an open architecture for collaborative filtering of netnews. CSCW '94

49

Proceedings of the 1994 ACM conference on Computer supported cooperative work (pp. 175-186). ACM.

Ricci, F., Rokach, L., & Shapira, B. (2011). Recommender Systems Handbook.

Springer US.

Sarkaleh, M., Mahdavi, M., & Baniardalan, M. (2012). Designing a Tourism

Recommender System Based on Location, Mobile Device and User Features in Museum. International Journal of Managing Information Technology, pp.

13-21.

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th international conference on World Wide Web , pp. 285-295. ACM.

Schafer, J., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative Filtering Recommender Systems. The Adaptive Web, pp. 291-324.

Schilit, B. N., Adams, N., & Want, R. (1994). Context-Aware Computing Applications.

WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications , pp. 85-90. IEEE.

Su, X., & Khoshgoftaar, T. M. (2009, 1 1). A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, p. doi:10.1155/2009/421425.

Tintarev, N., & Masthoff, J. (2007). A Survey of Explanations in Recommender Systems. ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop , pp. 801-810. Istanbul: IEEE.

Tyagi, S., & Bharadwaj, K. K. (2012). Enhanced New User Recommendations based on Quantitative Association Rule Mining. Procedia Computer Science, pp.

102-109.

Vozalis, E., & Margaritis, K. G. (2003). Analysis of recommender systems algorithms.

Proc. of the 6th Hellenic-European Conference on Computer Mathematics and its Applications-HERCMA.

Wang, S.-L., & Wu, C.-Y. (2011). Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system. Expert Systems with Applications, pp. 10831–10838.

Woerndl, W., & Schlichter, J. (2007). Introducing context into recommender systems.

Proceedings of AAAI Workshop on Recommender Systems in E-Commerce, pp.

138-140.

Zhou, S., Zhou, X., Yu, Z., Wang, K., Wang, H., & Ni, H. (2009). A Recommendation Framework towards Personalized Services in Intelligent Museum. 2009

International Conference on Computational Science and Engineering , pp. 229 - 236. IEEE .

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網站資源

國立臺灣史前文化博物館。上網日期:104 年 6 月 28 日,檢自:

http://www.nmp.gov.tw/exhibition/permanent/nmp/mission.php

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