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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 A Daily-Life Activity Assistant–Providing a Dynamic Recommender Service based on Multi-dimensional Filtering |
Authors: | Yueh-Hsun Wu 吳岳勳 |
Advisor: | 曹承礎(Seng-Cho Chou) |
Keyword: | 推薦系統,多維度,境資訊,協同過濾,概念階層, Recommender System,Multi-dimension,Contextual Information,Collaborative Filtering,Concept Hierarchy, |
Publication Year : | 2007 |
Degree: | 碩士 |
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。 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. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29847 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 資訊管理學系 |
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File | Size | Format | |
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ntu-96-1.pdf Restricted Access | 3.36 MB | Adobe PDF |
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