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研究生:張文慈
研究生(外文):CHANG,WEN-TZU
論文名稱:餐廳信任基礎推薦機制之研究
論文名稱(外文):A Study on the Trust-based Restaurant Recommendation Mechanism
指導教授:吳濟聰吳濟聰引用關係
口試委員:吳濟聰林文修廖耕億
口試日期:2013-11-21
學位類別:碩士
校院名稱:輔仁大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:106
中文關鍵詞:信任機制社會網路協同過濾個人化
外文關鍵詞:Trust MechanismSocial NetworksCollaborative FilteringPersonal
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Web2.0技術的發展,內容也愈趨龐雜,然推薦方式若僅採用大眾累積推薦對個人已略顯不足,而協同為主的技術也存有資料稀疏性(data sparseness)、冷啟動(cold start)及延展性(scalability)等問題。
遂本研究建置一個信任機制(trust mechanism)的社會網路推薦系統,希望能改善協同過濾演算法之績效。透過實體社會中的信任關係結合網路社群成員間的口味相似度,讓每個人皆可藉著朋友群的概念得到推薦之效果,以降低傳統協同推薦需要有相同的項目評分方能進行計算所造成的資料稀疏性問題。
本研究以模擬實驗法的方式進行實驗,透過讓使用者自行選擇覺得相似程度高的朋友作為推薦者,並計算朋友群的餐廳偏好作為推薦模組的推薦依據。結果與計算餘絃相似度(cosine similarity)的協同過濾推薦及熱門餐廳技術相比,顯示在比較相同預測目標的情況下,本研究所提出的推論方法確能夠有效地挖掘使用者的潛在偏好餐廳並可降低延展性更能提供有效的推薦品質且不需要繁瑣的計算並具個人化的優勢,代表由使用者自行選擇喜好相似值的做法是可行的。

The content has become more kinds of complex as Web 2.0 was becoming more and more developed. It is insufficient with recommendation technologies which use accumulation of public for personalized recommendations. Then Collaborative Filtering has some problems with recommendation process, such as data sparseness,cold start and scalability.
The main objectives of study to establish Trust Mechanism of Recommendation System that based on Social Networks to improve the performance of Collaborative Filtering algorithms. It can get great recommendation effect and reduce data sparseness problem which can be calculated by same score of item for traditional Collaborative Filtering methods through friends group which combine taste similar to food in virtual community.
In this study, we use experiments of simulation through user pick high degree of taste similarity for friends as recommender and calculate restaurant preferences for group of friends as recommendation model themselves. Compared with Collaborative Filtering which calculates the result of cosine similarity and popularity-based recommendation under same prediction item, we propose a recommendation method that can effectively prove user’s potential preferences for restaurant and accuracy of the recommendations quality which has advantage of personal represented without tedious calculations and reduced scalability. The results that pick taste similarity themselves are feasible.

表次 vi
圖次 viii
第壹章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的 3
第四節 研究流程 4
第貳章 文獻探討 7
第一節 餐廳相關研究 7
第二節 信任網路(Trust Networks) 9
第三節 推薦系統(Recommendation System) 22
第四節 推薦效率評估及衡量 24
第參章 研究方法 27
第一節 研究架構 27
第二節 實驗設計 29
第三節 建立以信任圈為基礎之推薦機制 30
第四節 信任模組 32
第五節 與餐廳偏好計算推薦計算方式實例說明 35
第肆章 實驗結果與資料分析 45
第一節 實驗與系統架構說明 45
第二節 實驗系統參數設定 51
第三節 實驗流程及進行方式 66
第四節 實驗結果與分析 67
第伍章 結論與建議 89
第一節 研究結論 89
第二節 研究限制 91
第三節 研究貢獻 92
第四節 未來研究方向 93
參考文獻 94
附錄一 100
附錄二 103
附錄三 106

一、中文部分
1.朱惠英,台南市上班族之生活型態與香草餐廳消費者決策行為之研究,建國科大學報,25(3),2006,頁45-70。
2.李永銘、李宗穎、陳正乾,信任機制為基礎之即時通訊系統,第十二屆資訊管理暨實務研討會,2006,頁1-15。
3.吳虹瑩,朋友圈餐廳推薦機制之研究,輔仁大學資訊管理學系碩士論文,2012。
4.張毓倫,個人化顯隱性知識推薦方法之研究,成功大學資訊管理研究所學位論文,2003。
5.張火燦、劉淑寧,從社會網路理論探討員工知識分享,人力資源管理學報,2(3), 2002,頁101-113。
6.黃河銓、王群元,以社會網路與電影本體為架構之電影推薦系統,電子商務學報,12(4),2010,頁595-620。
7.蔡長清、 許淑芬,台灣都會區美食餐廳顧客之體驗價值與品牌績效之相關研究, 商業現代化學刊,6(2),2011,頁43-57。
8.陳玲玉,整合信任網路與回饋機制之推薦系統-以餐廳推薦應用為例,國立高雄應用科技大學訊管理學系碩士論文,2008。
9.陳思妤,以Facebook好友為基礎的音樂推薦研究,輔仁大學資訊管理學系碩士論文,2012。
10.陳琪婷、李劼翰,餐廳消費者購買涉入程度與購買決策關係之研究,人類發展與家庭學報,(10) ,2008,頁1-29。

二、英文部分
1.Adomavicius, G. & Tuzhilin, A., Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,IEEE Transactions on Knowledge & Data Engineering, 17(6), 2005, pp.734-749.
2.Basu, C., Hirsh, H. & Cohen, W., Recommendation as classification: Using social and content-based information in recommendation,Proceedings of the Fifteenth National Conference on Artificial Intelligence,1998, pp.714-720.
3.Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. & Zanasi, A, Discovering Data Mining from Concept to Implementation, Vol. 1. Upper Saddle River, NJ: Prentice Hall, 1998.
4.Cho, H. Y. & Kim, J. K., Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications, 26(2), 2004, pp.233-246.
5.Cho, Y. B., Cho, Y. H. & Kim, S. H.,Mining Changes in Customer Buying Behavior for Collaborative Recommendations, Expert Systems with Applications, 28(2), 2005, pp.359-369.
6.Dell' Amico,M., & Capra, L.,SOFIA: Social filtering for robust recommendations, IFIPTM 2008/Joint iTrust and PST Conference on Privacy, Trust Management and Security, 2008, pp.135-150.
7.Ding, L., Kolari, P., Ganjugunte, S., Finin, T. & Joshi, A., Modeling and Evaluating Trust Network Inference, Seventh International Workshop on Trust in Agent Societies, 2004,pp.1-13.
8.Golbeck, J. & Hendler, J., Reputation Network Analysis for Email Filtering, Proceedings of the First Conference on Email and Anti-Spam, 2004,pp.1-8.
9.Golbeck, J. & Hendler, J., Film Trust: Movie recommendations using trust in Web-based social networks, proceedings of 3rd IEEE Consumer Communications and Networking Conference,26, 2006,pp.1-5.
10.Guha, R., Kumar, R., Raghavan. P. & Tomkins, A., Propagation of trust and distrust. In: Feldman SI, Uretsky M, Najork M, Wills CE, eds, Proc. of the 13th Int’l Conf. on World Wide Web, New York: ACM Press, 2004,pp.1-10.
11.Haubl, G. & Murray, K. B., Personalized product recommendations and consumer purchase decisions [position paper],Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research. International Conference on Intelligent User Interfaces (IUI 2005), San Diego, California, 2005, pp.9-12.
12.Herlocker, J. L., J. A. Konstan, L. G. Terveen, & Riedl, J. T., Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems, 22(1), 2004,pp.5-53.
13.Hu, R. & Pu, P., A Comparative User Study on Rating vs. Personality Quiz based Preference Elicitation Methods, In Proceedings of the 14th international conference on Intelligent user interfaces, 2009, pp.367-372.
14.Huang, Z., Chen, H. & Zeng, D., Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Transactions on Information Systems, 22(1), 2004, pp.116-142.
15.Hung, L. P., A Personalized Recommendation System based on Product Taxonomy for One-to-One Marketing Online,Expert Systems with Applications, 29(2), 2005, pp. 383-392.
16.Iain, E. & Richardson, G., Video Codec Design: Developing Image and Video Compression Systems, Wiley, 2002,pp1-313.
17.Jamali, M. & Ester, M., Trustwalker: A r & om walk model for combining trust-based and item-based recommendation, In KDD’09: The 15th ACM SIGKDD conference on Knowledge Discovery and Data Mining, 2009,pp.1-9.
18.Konstas, I., Stathopoulos, V. & Jose, J. M., On social networks and collaborative recommendation, Proceedings of the 32nd international ACM SIGIR conference on Research & development in in3kxformation retrieval, 2009, pp.195-202.
19.Lee , S. K. , Cho, Y. H. & Kim, L. H.,Collaborative Filtering with Ordinal Scalebased Implicit Ratings for Mobile Music Recommendations, Information Sciences, 180(11), 2010 , pp.2142-2155.
20.Marsh, S., Formalising Trust as a Computational Concept, Ph.D. dissertation, University of Stirling, 1994,pp.1-184.
21.Massa, P. & Bhattacharjee, B., Using trust in recommender systems: an experimental analysis, Paper presented at the Proceedings of 2nd International Conference on Trust Managment, Oxford, England , 2004,pp.1-15.
22.Meo, P. D., Nocera, A., Rosaci, D. & Ursino, D., Recommendation of reliable users, social networks and high-quality resources in a social internetworking system. AI Commun, 24(1),2011, pp. 31-50.
23.Mui, L., Mohtashemi, M. & Ari Halberstadt, A Computational Model of Trust and Reputation, In 35th Hawaii International Conference on System Science, 2002,pp.1-139.
24.Oard, D. W. & Marchionini, G., A conceptual framework for text filtering.,Technical Report EE-TR-96-25, CAR-TR-830, CLIS-TR-96-02, CS-TR-3643,1996, pp.1-32.
25.Oliveira, A. D. R., Bessa, L.N., Andrade, T. R., Filgueiras, L. V. L. & Sichman, J. S., Trust-based recommendation for the social web[J], IEEE Latin America Transactions, 10(2) ,2012, pp.1661-1666 .
26.Papagelis, M., Plexousakis, D. & Kutsuras, T., Alleviating the sparsity problem of collaborative filtering using trust inferences, presented at the Proceedings of the Third international conference on Trust Management, 2005, pp.224-239.
27.Pham, M. C., A clustering approach for collaborative filtering recommendation using social network analysis[J], Journal of Universal Computer Science, 17(4), 2011, pp.583-604.
28.Pitsilis, G. & Marshall, L., Trust as a key to improving Recommendation Systems, Computer Science, 2004, pp.1-15.
29.Procter, R. & MacKinlay, A., Social Affordances and Implicit Ratings for Social Filtering on the Web, Proceedings of the 5th DELOS Workshop on filtering and Collaborative Filtering, 1997, pp.1-8.
30.Rashid, A. M., Albert, I., COSley, D., Lam, S. K., McNee, S. M., Konstan, J.A. & Riedl, J., Getting to know you: learning new user preferences in recommender systems,Proceedings of the IUT 02 ACM ,2002, pp.127-134.
31.Sarwar, B., Karypis, G., Konstan, J. & Riedl, J., Analysis of Recommendation Algorithms for e-Commerce, Proceedings of The 2nd ACM Conference on Electronic Commerce, 2000, pp.158-167.
32.Swearingen, K. & Sinha, R., Beyond Algorithms: An HCI Perspective on Recommender Systems, ACM Workshop on Recommender Systems,2001, pp.1-10.
33.Sweetser, P. & Wyeth, P., GameFlow: a model for evaluating player enjoyment in games. ACM Computers in Entertainment(CIE), 3(3), 2005, pp.1-24.
34.Yuan, Q., Chen, L. & Zhao, S., Factorization vs. regularization:fusing heterogeneous social relationships in top-n recommendation, In RecSys’11, 2011, pp. 245-252.
35.Walter, F. E., Battiston, S. & Schweitzer, F., A model of a trust-based recommendation system on a social network, Autonomous Agents and Multi-Agent Systems, 16(1), 2008, pp.57-74.
36.Wei, Z. A novel trust model based on recommendation for E-Commerce, in International Conference on Service Systems and Service Management 2007, Chengdu, 2007, pp.1-4.
37.Zarghami, A., Fazeli, S. & Dokoohaki, N., Social trust-aware recommendation system: At-index approach[C],2009 IEEE/WIC/ACM International Joint Conferences on Web Intelligence(WI) and Intelligent Agent Technologies( IAT), 2009, pp.85-90.

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