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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹承礎 | |
| dc.contributor.author | Ting-Kuang Chen | en |
| dc.contributor.author | 陳亭光 | zh_TW |
| dc.date.accessioned | 2021-06-14T17:20:14Z | - |
| dc.date.available | 2008-08-05 | |
| dc.date.copyright | 2008-08-05 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-24 | |
| dc.identifier.citation | 英文部分
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[18] Järvinen, A., Heilö, S., and Mäyrä, F., “Communication and Community in Digital Entertainment Services,” University of Tampere: Hypermedia Laboratory Net Series, 2002. [19] Lazzaro, N., “Why We Play Games: Four Keys to More Emotion Without Story,” XeoDesign White paper, 2004. [20] Manouselis, N. and Costopoulou, C., “Analysis and Classification of Multi-Criteria Recommender Systems,” World Wide Web, Vol. 10, pp. 415-441, 2007. [21] Pazzani, M., “A Framework for Collaborative, Content-Based, and Demographic Filtering,” Artificial Intelligence Review, Vol. 13, pp. 393-408, 1999. [22] Pine II, B. J. and Gilmore, J. H., “Welcome to the Experience Economy,” Harvard Business Review, pp. 97-105, July-August 1998. [23] Resnick, P. and Varian, H. R., “Recommender Systems,” Communications of the ACM, Vol. 40(3), 56-58, 1997. [24] Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proceedings of ACM CSCW’94 Conference on Computer-Supported Cooperative Work, pp. 175-186, 1994. [25] Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., “Analysis of Recommendation Algorithms for E-Commerce,” Proceedings of the ACM E-Commerce 2000 Conference, pp. 158-167, 2000. [26] Schafer, J. B., Konstan, J., Riedl, J., “Recommender systems in E-Commerce,” E-Commerce of ACM, 1999. [27] Schafer, J. B., Konstan, J., Riedl, J., “E-commerce recommendation applications,” Data Mining and Knowledge Discovery, Vol. 5(1-2), pp. 115-153, 2001. [28] Shardanand, U. and Maes, P., “Social Information Filtering: Algorithms for Automating ‘Word of Mouth’,” Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems, Vol. 1, pp. 210-217, 1995. [29] Sweetser, P. and Wyeth, P., “GameFlow: a model for evaluating player enjoyment in games,” ACM Computers in Entertainment (CIE), 2005, Vol. 3(3). [30] Teng, W. G. and Lee, H. H., “Collaborative Recommendation with Multi-Criteria Ratings,” Journal of Computer (電腦學刊), Vol. 17(4), pp. 69-78, 2007. [31] Yee, N., “Motivations of Play in Online Games,” CyberPsychology and Behavior, Vol. 9(6), pp. 772-775, 2006 中文部分 [32] Rusel DeMaria, Johnny L. Wilson著，蔣鏡明、李宜安 校譯，圖解電子遊戲史，2002 [33] 小南，「骨灰達人的電玩歷程」，電腦玩家雜誌達人碎碎念專欄，Vol. 194, pp.158-159, Vol.195, pp. 142-143, Vol. 196, 144-145, 2007 [34] 張武成，民91，線上遊戲軟體設計因素與使用者滿意度關聯之研究，私立淡江大學資訊管理學系研究所碩士論文 [35] 陳建文，民94，電子遊戲產業分析與臺灣遊戲廠商之策略研究，國立台灣大學國際企業學研究所碩士論文 網站部分 [36] Gamespot http://www.gamespot.com [37] Gamezone http://www.gamezone.com [38] Zagat’s Gudie http://www.zagat.com [39] 巴哈姆特電玩資訊站 http://www.gamer.com.tw | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41151 | - |
| dc.description.abstract | 自推薦系統發展以來，隨其技術之進步，已廣泛應用於多種電子商務及個人活動之領域，包含電影、新聞、書籍、旅遊、飲食、休閒活動等。然而，電子遊戲為推薦系統尚未觸及之新興娛樂，僅視為相同於一般商品推薦可延伸之服務，但玩遊戲強調於使用者主動參與、個人技能養成、社群互動，產生沉浸效果，每位使用者所重視的因素皆不相同，因此使用者與遊戲產品的互動更為複雜化。
在使用者與產品、服務互動或從事特定活動的過程中，個人接受到的經驗亦對最終的喜好及滿意程度產生影響。當個人給予一項產品或服務負面評價時，可能僅是對過程中的特定經驗感到不快，不願再次接觸相同的經驗，而並非不滿意或排斥項目本身，但此現象無法在現有以單一項目評分為基礎的推薦系統中反映出來。 本研究採納依沉浸理論發展的遊戲沉浸模型，設計為衡量使用者經驗的準則，界定出適於遊戲推薦系統中衡量經驗之標準，作為多準則評分推薦系統的基礎，並提出運用各項經驗準則與總評分之間的相似度，視為調整使用者對各經驗準則重要性之權重，改良推薦預測的結果，應用於實作之遊戲推薦系統，並依實驗結果的數據分析與使用者回饋問卷兩方面，衡量出基於使用者經驗的推薦結果，更能貼近使用者的喜好與需求。本研究主要指標評估結果F1準確率值達86.25%，使用者對系統推薦的整體滿意度亦達73%。 | zh_TW |
| dc.description.abstract | Recommendation systems have been widely used for e-commerce and personal activities, including films, news, books, traveling and restaurants. Nevertheless, electronic games are new recreations which recommendation systems haven’t been applied to yet. Most people merely regard games recommendation as the extension of other commodities, while what really counts in playing games is active participation of users, cultivation of personal skill, interaction of communities, and immersion effects, hence the interaction between users and products are more complex.
Furthermore, personal experiences will certainly influence one’s preference and satisfaction during the period of user-product interaction. If a user gives a product or service a negative evaluation, one may merely feel unpleasant about some certain experience, and is unwilling to attain the same experience again. It doesn’t mean that the user is completely unsatisfied with the item, but this kind of situation cannot be reflected on the single criterion rating recommendation system. This research adopts GameFlow Model which is based on Flow Theory to devise the criteria of user experience measurement, and use them for the rating elements of multi-criteria recommendation system. We adjust the criteria weight by analyzing the similarities between criteria and overall rating to enhance the result of prediction and prove the usefulness of our system. The primary measurements, F1-measure and users’ system satisfaction rate, reach to 86% and 73% respectively. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-14T17:20:14Z (GMT). No. of bitstreams: 1 ntu-97-R95725001-1.pdf: 2702748 bytes, checksum: aa807b39f478c26634520fbed30ca0b8 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 第一章 緒論 1
第一節 研究背景與動機 1 第二節 研究目的 4 第三節 研究流程 5 第二章 文獻探討 7 第一節 推薦系統 7 2.1.1 內容導向式推薦 9 2.1.2 協同過濾式推薦 11 2.1.3 混合式推薦 13 2.1.4 多準則評分推薦 14 第二節 電子遊戲 18 2.2.1 遊戲定義與遊戲特性 18 2.2.2 電子遊戲發展歷程 21 2.2.3 遊戲分類法 26 第三節 使用者經驗 29 2.3.1 經驗的定義 29 2.3.2 遊戲中的使用者經驗 30 第三章 系統設計 35 第一節 研究模型與研究方法 35 第二節 系統架構與推薦流程 38 第三節 系統單元設計 41 3.3.1 行為描繪模組 41 3.3.2 相似度模組 45 3.3.3 經驗導向學習模組 47 3.3.4 推薦模組 49 第四章 實驗分析 53 第一節 系統開發實作 53 第二節 實驗方法 54 4.2.1 實驗流程 54 4.2.2 評估方法與指標 56 4.2.3 系統功能與使用者參與實驗 59 第三節 實驗結果分析 63 4.3.1 初步分析 63 4.3.2 受試者資料分析 66 4.3.3 實驗結果分析 69 第四節 實驗整體探討 75 第五章 結論與建議 77 第一節 結論與貢獻 77 第二節 研究限制 78 第三節 未來發展與建議 79 參考文獻 81 | |
| dc.language.iso | zh-TW | |
| dc.subject | 使用者經驗 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 電子遊戲 | zh_TW |
| dc.subject | 多準則評分 | zh_TW |
| dc.subject | 協同過濾 | zh_TW |
| dc.subject | Multi-criteria Ratings | en |
| dc.subject | Recommendation System | en |
| dc.subject | User Experience | en |
| dc.subject | Collaborative Filtering | en |
| dc.subject | Electronic Games | en |
| dc.title | 基於使用者經驗之多準則評分遊戲推薦系統 | zh_TW |
| dc.title | A Multi-criteria Game Recommendation System
Based on User Experience | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 許瑋元,吳玲玲 | |
| dc.subject.keyword | 推薦系統,電子遊戲,多準則評分,協同過濾,使用者經驗, | zh_TW |
| dc.subject.keyword | Recommendation System,Electronic Games,Multi-criteria Ratings,Collaborative Filtering,User Experience, | en |
| dc.relation.page | 84 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-07-27 | |
| 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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