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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
dc.contributor.author | Yu-Rung Shiue | en |
dc.contributor.author | 薛鈺蓉 | zh_TW |
dc.date.accessioned | 2021-06-15T06:16:11Z | - |
dc.date.available | 2013-08-19 | |
dc.date.copyright | 2010-08-19 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-11 | |
dc.identifier.citation | [中文部分]
[1] 洪智力、侯彥旭。2009。個人化電子口碑推薦系統─應用推敲可能性理論。中原大學資訊管理學系。 [2] 陳正德。2004。以項目為基礎的協同過濾應用於網路教材瀏覽推薦之研究。銘傳大學資訊工程學系碩士論文。 [3] 黃君德。2002。電子商業網站產品推薦系統的研究與實作。臺灣大學資訊工程學研究所碩士論文。 [4] 楊亨利、張文祥。2008。多維度推薦系統: 應用至行事曆助理。Journal of e-Business, 第十卷 第一期,pp. 275-304。 [5] 蔡期輝。2007。社會影響與衝動性購買特質對衝動性購買反映之影響。東吳大學心理學系碩士論文。 [英文部分] [6] C. P. Lam. SNACK: Incorporating Social Network Information in Automated Collaborative Filtering. The fifth ACM Conference on Electronic Commerce (EC ’04), pp. 254-255, 2004. [7] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using Collaborative Filtering to Weave an Information TAPEDTRY. Communications of the ACM, Vol. 35, No. 12, pp. 61-70, 1992. [8] George Karypis. Evaluation of Item-Based Top-N Recommendation Algorithms. Proceedings of the tenth international conference on Information and knowledge management, 2001. [9] H. H. Hyman. The psychology of status. Archives of Psychology, No. 269, 1942. [10] J. B. Schafer, J. Konstan, and J. Riedl. Recommender System in E-Commerce. Proceeding of The First ACM Conference in Electronic Commerce, pp. 158-166, 1999. [11] J. J. Argo, K. White, and D. W. Dahl. Social comparison theory and deception in the interpersonal exchange of consumption information. Journal of Consumer Research, 33,99-108, 2006. [12] J. L. Herlocker, J. A. Konstan, K. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1):5 -53, 2004. [13] M. Balabanovic and Y. Shoham. Combining Content-Based and Collaborative Recommendation. Communication of the ACM, 1997. [14] M. Jamali and M. Ester. Using a Trust Network to Improve Top-N Recommendation, Proc. 3rd ACM Conf. on Recommender Systems, (RecSys ‘2009), New York City, NY, USA, 2009. [15] P. Massa and B. Bhattacharjee. Using trust in recommender systems: an experimental analysis. Lecture Notes in Computer Science, 2004. [16] P. Massa and P. Avesani . Trust-aware recommender system. Proceedinga of the 2007 ACM conference on Recommender systems, pp. 17-24, 2007. [17] Q. Li and B. M. Kim. Clustering Approach for Hybrid Recommender System. Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI’03), 2003. [18] S. S.Wrng and M. J. Liu. Feature-based Recommendation for One-to-One Marketing. Expert Systems with Applications, Vol. 26, No. 4, pp. 493-508, 2004. [19] S. Senecal and J. Nantel. The influence of online product recommendations on consumers' online choices. J. Retailing, v80, pp. 159-169, 2004. [20] T. F. Mangleburg, P. M. Doney, and T. Bristol. Shopping with friends and teens’ susceptibility to peer influence. Journal of Retailing, 80, 101-116, 2004. [21] The Art, Science and Business of Recommendation Engines, ReadWriteWeb, 2007. [22] Ting-Chun Peng and Seng-Cho T. Chou. iTrustU: A Blog Recommender System Based on Multi-faceted Trust and Collaborative Filtering. SAC’09, 2009. [23] W. O. Bearden and M. J. Etzel. Reference group influence on product and brand purchase decisions. Journal of Consumer Research, 9 (September), 183–94, 1982. [24] X. Amatriain, J. M. Pujol, and N. Oliver. I like it... i like it not: Evaluating user ratings noise in recommender systems. In Proc. of UMAP'09, 2009. [網路部分] [25] Roger, 推薦系統的分類 (Obsolete), from http://blurkerlab.blogspot.com/2007/10/blog-post_08.html, 2007. [26] Roger, 如何評估推薦系統(一), from http://blurkerlab.blogspot.com/2008/01/blog-post_25.html, 2008. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47747 | - |
dc.description.abstract | 推薦系統的研究已行之有年,主要以內容導向過濾法、協同式過濾法與混合式過濾法等為基本分類。近來由於社群網路的興盛,許多研究紛紛開始注意到,加入社會影響的考量,如最具代表性的信任機制,在面對冷起始的情況下,更能顯著提升推薦效能。
對協同式推薦系統來說,建造使用者輪廓的資訊,其來源從使用者本身提供的喜好評分資訊,透過這些喜好與他人的喜好相似度,預測使用者可能會喜歡的商品;信任機制方面,則是利用非直接的管道,設想使用者與高度信任的對象在喜好興趣方面相似的可能性較高,歸納而言,主要的研究精神皆在於「如何找尋更多與使用者喜好正相關的推薦依據」,故本研究希望能從「如何拓展直接掌握使用者興趣喜好之來源」的另一種角度切入,找出可以再提升推薦效能的方法。 為此本研究期望透過近來社群網路興盛之便,利用社群網路的交友機制,蒐集使用者朋友對使用者的主動推薦商品資訊,讓系統除了能從使用者本身商品評分外,亦可由朋友的主動推薦掌握使用者可能會有的興趣,是故本研究提出Social-based Adjustment Recommender System(SARS)演算法,將朋友對某商品的主動推薦次數,融入傳統協同式User-based演算法計算出的預測分數中,將其作為最終推薦預測結果。實驗結果證實,利用使用者朋友的主動推薦作為推薦系統預測依據之一,確實能達到推薦預測準確率提升效果。 | zh_TW |
dc.description.abstract | Recommender system has been studied for many years. The basic types of recommender system are: content-based, collaborative filtering, and hybrid approach. Recently, with the prosperity of social network, many studies begin to notice that the recommendation accuracy can be enhanced by taking social influence such as trust mechanism into consideration, especially when there is a cold start problem.
For collaborative filtering recommender system, the source to construct user profile comes from rating information based on user's personal preference. The items we recommend to a person are based on the similarity between preferences of users. As for trust mechanism, it is assumed that the similarity between users' preferences positively correlate with their trust relationship. In sum, the main idea is focus on finding a recommender support which has positive relative with users’ preferences. Therefore, our work aims to enhanced recommendation accuracy by increasing the source of users’ preferences that system can directly get. Our study tries to use social network’s friends-making mechanism to collect user’s interest through user’s friends’ active opinions. Recommender system can analyze user’s preference not only from user’s ratings, but also from friends’ active recommendations. So we propose social-based adjustment recommender system (SARS) algorithm, which merged the counts of friends’ active recommendations into the score predicted by user-based collaborative filtering as final predict score. According to experiment result, we prove that recommendation accuracy can be improved by considering friends’ active opinions. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T06:16:11Z (GMT). No. of bitstreams: 1 ntu-99-R97725001-1.pdf: 3533505 bytes, checksum: b9b5f038b510b7ff9edbd364590d0816 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 中文摘要 III
Abstract IV 目錄 VI 表次 VIII 圖次 IX 第一章、 緒論 1 1.1 研究動機 1 1.2 研究目的 3 第二章、 文獻探討 4 2.1 社會影響 4 2.2 推薦系統 6 2.2.1 內容導向過濾法 7 2.2.2 協同過濾法 8 2.2.3 混合式推薦 12 2.3 考量社會影響的推薦機制 13 第三章、 系統設計 15 3.1 研究方法 15 3.2 系統架構 18 3.3 系統單元設計 19 3.3.1 行為描繪模組與交友推薦模組 19 3.3.2 交友推薦模組 20 3.3.3 相似度模組 21 3.3.4 推薦模組 22 第四章、 實驗分析 24 4.1 實驗方法 24 4.1.1 實驗流程 24 4.1.2 評估方法與指標 26 4.1.3 系統功能與使用者實驗參與 28 4.2 實驗結果分析 33 4.2.1 受試者與評分資料概況 33 4.2.2 不同資料量之準確性分析 34 4.2.3 不同演算法推薦準確率之比較 36 4.3 實驗整體探討 41 第五章、 結論與建議 43 5.1 結論與貢獻 43 5.2 研究限制 43 5.3 未來發展與建議 45 參考文獻 46 | |
dc.language.iso | zh-TW | |
dc.title | SARS-結合朋友主動資訊之推薦機制 | zh_TW |
dc.title | Social-based Adjustment Recommender System- Increasing Recommendation Accuracy by Combining Friends’ Opinions with User-based Collaborative Filtering | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 杜志挺,吳玲玲 | |
dc.subject.keyword | 推薦系統,協同過濾,社群網路,社會影響, | zh_TW |
dc.subject.keyword | recommender system,collaborative filtering,social network,social influence, | en |
dc.relation.page | 48 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2010-08-11 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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