以自建本體進行使用者興趣偵測與文件推薦;Automatically Constructing Ontology for Detecting User’s Interests and Document Recommendation

NCUIR > School of Management at National Central University > Graduate Institute of Information Management > Electronic Thesis & Dissertation > Item 987654321/68814

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68814

Title:	以自建本體進行使用者興趣偵測與文件推薦;Automatically Constructing Ontology for Detecting User’s Interests and Document Recommendation
Authors:	江欣鴻;Chiang,Hsin-Hung
Contributors:	資訊管理學系
Keywords:	推薦系統;本體;使用者輪廓;Recommendation systems;Ontology;User profile
Date:	2015-07-27
Issue Date:	2015-09-23 14:31:29 (UTC+8)
Publisher:	國立中央大學
Abstract:	推薦系統是一個常見的資訊過濾系統，不管於商業或個人都是非常重要的技術，可以依據使用者不同的興趣給予不同的物件。本體(Ontology)能將相同領域內的同一套概念進行概念化表達。本研究藉由自動建立本體，讓眾多未分類的文件進行階層式的分群，並應用於使用者輪廓的建立，把使用者的興趣對應至本體進行階層式分群管理。另外，本研究改善使用者興趣於本體上的對應，並加入長期興趣的考量，使得使用者興趣的偵測更準確。在推薦方面，本研究利用本體中的結構相似度來找出使用者的隱性興趣，讓推薦的文件更多樣化。本研究於實驗中採用亞馬遜網路商店的書籍簡介當作資料集，並模擬不同情況下，使用者閱讀文件而產生的興趣變化，由實驗結果得出本系統可以改善推薦準確度並能讓推薦的文件更多樣。期望本系統能用於商業層面，讓企業更準確得知顧客的興趣並帶來更大的利益。;Recommended system is a common information filtering system. It’s a very important technology no matter in business or person, and it can provide users different documents according to their different interests. We can use ontology to conceptualize the concepts within the same domain. This study automatically construct ontology to let the unclassified document hierarchical clustering and applied to create user profile. Users’ interests can be mapped to ontology in order to manage interests by hierarchical clustering. Besides, this study improve the method to mapping user’s interests to ontology and added long term interest into the method to make the detection of users’ interests more accurate. In the recommendation, this study use structure similarity at the ontology to find the implicit interests and let the recommendation of documents be more diverse. In the experiments, this study use book’s description on Amazon online shopping websites as data collection and simulate the change of users’ interest in different conditions. We could find that this study can improve the accurate of recommendation and make recommendation more diverse. Expects this system to be used for commercial, let enterprises to more accurately know the customer′s interest and bring more benefits.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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