結合層級分析法與圖像呈現之推薦模型建立

Designing Recommendation Model with Analytic Hierarchy Process (AHP) and Image-based Presentations.

陳佳駿(Jia-Jun Chen)

指導教授 ： 曾俊元

國立臺北大學/商學院/資訊管理研究所/碩士(2012年)

摘要

本研究為網路推薦系統建立在網頁平台時，以往過濾方式對於使用者需求仍不精確，影響使用者獲取推薦的正確性。為增進過濾方式，結合多種過去以臻成熟理論，加入層級分析法，建立推薦模型，並採用因素刪減方式加快運算速度。研究內容主要以學生為對象，以四種推薦方式為比較對象。本研究以T檢定檢測樣本中，使用者對四種推薦模型設計的滿意度，結果顯示，圖像輔助與成對比較確實有助於使用者獲得較精準推薦，與過去推薦模型設計相比，能有效提高推薦精確度。本研究之結果可助於產品需求調查，透過產品特性來調查使用者的需求比重，並讓使用者獲得更滿意的推薦。

關鍵字：層級分析法、圖像輔助、推薦模型、購買意願

關鍵字

層級分析法 ； 圖像輔助 ； 推薦模型 ； 購買意願

並列摘要

High accuracy of market surveys is critical because it often determines the marketing directions and strategies of a company. However, most customers are not clear to know what they really need. For company, it is hard to design or sell the appropriate products to customers if they could realize their demand. This paper applies Analytic Hierarchy Process (AHP) and image-based presentation to questionnaire design to give users instant alternative feedback with what they really need. From user decision-making behavior aspect, humans tend to make a more accurate preference decision in pair-wise factor comparison with precisely scoring to every factor. Furthermore, image-based presentations allow humans to imagine and visualize their real preference, and thus increase the accuracy and reliability of recommendations. In brief, this model focuses on improving the products’ reliability, performing real-time analysis, and increasing the purchasing intention. To evaluate the reliability of the model, the sample results pass the following tests: Cronbach’s Alpha test and pearson test. We verify all hypotheses by descriptive statistics, and T-test values of sample results show that users agree the statements of questions.

並列關鍵字

AHP ； Pair-wise factor comparison ； Image-based presentation ； Purchase intention ； BFR ； Instant feedback

參考文獻

14. Kun-Ching Lioa, Chan-Chia Wu, et al. (2006). " An Analysis of the Key Human Factor in Collision Accident on Maritime Casualty-Application Analytic Hierarchy Process." Hang Yun Ji Kan 15(4): 62-85.

1. CHEN, D. N., HU, P. J. H., KUO, Y. R. & LIANG, T. P. 2010. A Web-based personalized recommendation system for mobile phone selection: Design, implementation, and evaluation. Expert Systems with Applications, 37, 8201-8210.

2. CHO, Y. H., KIM, J. K. & KIM, S. H. 2002. A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23, 329-342.

3. WANG, Y. F., CHUANG, Y. L., HSU, M. H. & KEH, H. C. 2004. A personalized recommender system for the cosmetic business. Expert Systems with Applications, 26, 427-434.

4. CHEN, H. C. & CHEN, A. L. P. Year. A music recommendation system based on music data grouping and user interests. In, 2001. ACM, 231-238.