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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
| dc.contributor.author | Yueh-Hsun Wu | en |
| dc.contributor.author | 吳岳勳 | zh_TW |
| dc.date.accessioned | 2021-06-13T01:21:27Z | - |
| dc.date.available | 2007-07-24 | |
| dc.date.copyright | 2007-07-24 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-19 | |
| dc.identifier.citation | [ 1 ] Adomavicius, G., & Tuzhilin, A., “Extending recommender systems: A multidimensional approach,” IJCAI-01 Workshop on Intelligent Techniques for Web Personalization (ITWP’2001).
[ 2 ] Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A., “Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach,” ACM Transactions on Information Systems, Vol. 23, Issue 1, 2005, pp. 103-145. [ 3 ] Breese, Heckermen, and Kadie. Empirical analysis of predictive algorithms for collaborative filtering. Microsoft Research Technical Report, (MSR-TR-98-12), October 1998. [ 4 ] Chaudhuri, S. and Dayal, U., “An Overview of Data Warehousing and OLAP Technology,” ACM SIGMOD Record, Vol. 26, No.1, 1997, pp. 65-74. [ 5 ] Chen, H., Finin, T., 'An Ontology for a Context Aware Pervasive Computing Environment', IJCAI workshop on ontologies and distributed systems,Acapulco MX, August 2003. [ 6 ] Delgado, J., Ishii, N., and Ura, T. “Content-based Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents”, In Proc. Second Int. Workshop, CIA’98, 1998. [ 7 ] Dey, A. K., et al., Towards a Better Understanding of Context and Context-Awareness. Technical Report 99-22, Georgia Institute of Technology, 1999. [ 8 ] Goldberg, D., D. Nichols B.M.Oki, and D. Terry. , “Using collaborative filtering to weave an information tapestry”, Communications of the ACM, 35(12), 1992. [ 9 ] Henricksen, K, et al., Modeling Context Information in Pervasive Computing Systems. Proc. of the First International Conference on Pervasive Computing, (Pervasive'2002), Zurich, August 2002. [ 10 ] Herlocker, J. L. and Konstan, J. A. , “Content-Independent Task-Focused Recommendation,” IEEE Internet Computing, Vol. 5, No. 6, 2001, pp. 40-47. [ 11 ] Herlocker, J., Konstan, J., Borchers, A. and Riedl, J.,“An Algorithmic Framework for Performing Collaborative Filtering,”Proceedings of 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1999, pp.230-237. [ 12 ] Hyunjung Park, Jeehyong Lee, “A framwork of context-awareness for ubiquitous computing middlewares,” Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science(ICIS’05), pp369-374. [ 13 ] J. Ben Schafer, Joseph Konstan, and John Riedl, “Recommender Systems in E-Commerce”, E-Commerce of the ACM, 1999. [ 14 ] Jiawei Han , Micheline Kamber, Data mining: concepts and techniques, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000 [ 15 ] Johathan L., Herlocker, J., Konstan, J., and Riedl, J.,”Explaining Collaborative Filtering Recommendations,”ACM 2000 Conference on Computer Supported Cooperative Work,Dec.2000. [ 16 ] Jonathan L. Herlocker , Joseph A. Konstan , Loren G. Terveen , John T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.5-53, January 2004 [ 17 ] Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2):133–151, 2001. [ 18 ] Kimball, R., The Data Warehouse Toolkit, John Wiley & Sons, Inc., 1996. [ 19 ] M. Balabanovi and Y. Shoham. Fab: content-based, collaborative recommendation. Commun. ACM, 40:66~72, 1997. [ 20 ] M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems, 1999. [ 21 ] Meyer, S. , Rakotonirainy, A. “A Survey on Research on Context-Aware Homes,” Johnson, C., Montague, P. & Steketee, C. (eds.) Conferences in Research and Practice in Information Technology, Vol. 21. Australian Computer Society, 2003. [ 22 ] P. Dockorn Costa, “Towards a Service Platform for Context-Aware Applications”, Masters thesis, University of Twente, August 2003. [ 23 ] Ranganathan, Anand, et al. “A Middleware for Context-Aware Agents in Ubiquitous Computing Environments”, USENIX International Middleware Conference, 2002. [ 24 ] Resnick, P. and Varian, H. R.: 1997, “Recommender Systems”. Communications of the ACM, 40 (3), 56~58. [ 25 ] Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J., “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proceedings of the 1994 Computer Supported Cooperative Work Conference, Chapel Hill, 1994, pp. 175-186. [ 26 ] Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., Analysis of recommendation algorithms for e-commerce, Proceedings of the 2nd ACM conference on Electronic commerce, p.158-167, October 17-20, 2000, Minneapolis, Minnesota, United States [ 27 ] Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller,B., and Riedl, J.,“Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System,”Proceedings of the 1998 Conference on Computer Supported Cooperative Work, Nov 1998. [ 28 ] Schilit, B., Theimer, M. Disseminating Active Map Information to Mobile Hosts. IEEE Network, 8(5) (1994) 22-32 [ 29 ] 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, 1995, pp. 210-217. [ 30 ] Sunghoon Cho, Moohun Lee, Changbok Jang, Euiin Choi, Multidimensional Filtering Approach Based on Contextual Information, 2006 International Conference on Hybrid Information Technology(ICHIT’06), v2, p.497-504, 2006 [ 31 ] Tang, T. Y., Winoto, P., Chan, K. C. C., “On the Temporal Analysis for Improved Hybrid Recommendations,” Proceedings of the IEEE/WIC(WI’03), Halifax, 2003, pp. 214-220. [ 32 ] Tao Gu, Xiao Hang Wang, Hung Keng Pung, Da Qing Zhang, “An Ontology-based Context Model in Intelligent Environments “, Department of Computer Science, National University of Singapore, Singapore Connected Home Lab, Institute for Infocomm Research, Singapore [ 33 ] Wasfi, A. M. A.. “Collecting User Access Patterns for Building user Profiles and Collaborative Filtering”, In Int. Conf. On Intelligent User Interfaces, 1999. [ 34 ] Wenxian Wang, Weiyi Meng, Clement Yu. Concept Hierarchy Based Text Database Categorization in a Metasearch Engine Environment, First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1, 2000, pp. 0283 [ 35 ] Y. Y. Shih and D. R. Liu. Hybrid recommendation approaches: Collaborative filtering via valuable content information. In Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 8. IEEE Computer Society, 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29847 | - |
| dc.description.abstract | 從推薦系統的發展起源看來，其目的為解決資訊過剩(Information overload)問題，然而僅使用使用者與推薦內容兩項維度，未考量情境因素(Contextual Information)，然而隨著推薦內容的複雜性提升，情境因素的影響也逐漸提高，因此考慮情境因素的推薦機制有其存在之必要性。
本研究提出引入多維度情境資訊的活動推薦服務，透過考慮情境資訊之user profile形成的多維度架構，並利用彈性的概念階層以改善多維度相似度之計算，且運用協同過濾推薦(Collaborative Filtering)演算法來產生更符合個人化之活動推薦。同時本研究透過Web服務來實現SOA架構，以達成服務可攜性，讓所有的使用者可以容易地取得該服務，而開發者也可在任何平台上利用此服務。此外，我們對於系統產生的訓練資料和使用者身上取得的測試資料，也進行其合理性驗證與相關現象之觀察，作為本研究系統之實驗分析部分。 另外，有鑑於人口結構趨於高齡化之現象，居家照護的需求量也日益提高，可以利用此活動推薦系統，幫助被居家照護者推薦安排其日常生活的活動，因此本研究未來將可以應用在居家照護之領域上。 | zh_TW |
| dc.description.abstract | Initial recommender system was used to solve the information overload problem. However, the traditional recommender system only uses the two dimensions 'User' and 'Content', and not considers the importance of contextual information. With the increasing complexity of recommendation contents, the impact on decision of user is also on the rise. Therefore, considering contextual information in recommender system has its existing necessities.
We propose an activity recommendation service that includes multi-dimensional contextual information. It forms a multi-dimension architecture by user profiles that considers contextual information, uses flexible concept hierarchy to improve the multi-dimensional similarity computation, and applies the above two solution into collaborative filtering algorithm to make a more personalization activity recommendation. Besides, we adopt a service-oriented architecture (SOA) to build our system in order to provide a portable service. Then, every user and developer can access the service easily and use the service to develop applications in any platform. Also, in our system experiment analysis, we run rationality verification and observe the recommender system phenomenon for the training data that system collected or generated and the testing data that getting from users. Respecting the phenomenon of aging population, the need of homecare is gradually increasing. We can utilize this activity recommender system to help the family burden scheduling their daily-life activity better. Therefore, this research can be a potential application in homecare domain in the future. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T01:21:27Z (GMT). No. of bitstreams: 1 ntu-96-R94725044-1.pdf: 3443398 bytes, checksum: 7259f0c9a04b7307d57d4faf9f8988d5 (MD5) Previous issue date: 2007 | 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 2 第三節 研究流程 3 第二章 文獻探討 4 第一節 Context Awareness 4 2.1.1 Context定義 4 2.1.2 Context模式 5 2.1.3 Context-Aware基本架構 8 第二節 推薦系統 12 2.2.1 內容導向法 14 2.2.2 協同過濾法 15 2.2.3 綜合推薦法 20 第三節 多維度推薦模式 20 2.3.1 多維度(Multidimension) 21 2.3.2 評分推估技術 22 第四節 概念階層(Concept Hierarchy) 23 第三章 系統模型建構 25 第一節 系統架構 25 第二節 研究方法 26 第三節 系統設計目標 28 第四節 系統元件 30 3.4.1 行為描繪(Profile)建構模組 30 3.4.2 分群模組 35 3.4.3 多維度推薦模組 36 3.4.4 學習模組 38 3.4.5 規則式過濾模組 39 第四章 系統實作與實驗分析 40 第一節 系統使用情境 40 第二節 系統開發工具 41 第三節 系統實驗與分析 42 4.3.1 實驗預備 42 4.3.1.1 實驗流程與基本假設 42 4.3.1.2 受試者描述 44 4.3.1.3 推薦品質評估指標 45 4.3.2 系統功能 46 4.3.2.1 使用者基本資料 46 4.3.2.2 同儕群組 47 4.3.2.3 基礎前測(Warm Up) 47 4.3.2.4 取得推薦 48 4.3.3 實驗結果 49 4.3.3.1 預先資料 49 4.3.3.2 最小鄰群大小與NMAE 50 4.3.3.3 評分累積次數與NMAE 52 4.3.3.4 推薦機制與NMAE 54 4.3.3.5 採用率(Rate of Adoption) 55 4.3.3.6 使用者滿意度(Satisfaction) 56 第四節 整體探討 56 第五章 結論與建議 59 第一節 結論 59 第二節 研究限制 60 第三節 建議 61 參考文獻 62 | |
| dc.language.iso | zh-TW | |
| dc.subject | 境資訊 | zh_TW |
| dc.subject | 推薦系統 | zh_TW |
| dc.subject | 概念階層 | zh_TW |
| dc.subject | 協同過濾 | zh_TW |
| dc.subject | 多維度 | zh_TW |
| dc.subject | Contextual Information | en |
| dc.subject | Multi-dimension | en |
| dc.subject | Recommender System | en |
| dc.subject | Collaborative Filtering | en |
| dc.subject | Concept Hierarchy | en |
| dc.title | 以多維度過濾法為基礎之動態推薦服務－以日常生活活動助理為例 | zh_TW |
| dc.title | A Daily-Life Activity Assistant–Providing a Dynamic Recommender Service based on Multi-dimensional Filtering | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 吳玲玲(Ling-Ling Wu) | |
| dc.contributor.oralexamcommittee | 林俊叡 | |
| dc.subject.keyword | 推薦系統,多維度,境資訊,協同過濾,概念階層, | zh_TW |
| dc.subject.keyword | Recommender System,Multi-dimension,Contextual Information,Collaborative Filtering,Concept Hierarchy, | en |
| dc.relation.page | 64 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-19 | |
| 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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