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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳文進(Wen-Chin Chen) | |
dc.contributor.author | Tsung-Chieh Chang | en |
dc.contributor.author | 張琮傑 | zh_TW |
dc.date.accessioned | 2021-05-20T20:11:48Z | - |
dc.date.available | 2009-07-28 | |
dc.date.available | 2021-05-20T20:11:48Z | - |
dc.date.copyright | 2009-07-28 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-07-27 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9174 | - |
dc.description.abstract | 計算相似度(similarity)是研究上的熱門領域。以使用者為例,在計算使用者之間的相似度須先建立使用者描述(user profile)。在現今Web2.0的時代,使用者可以上傳自己的資料並用標籤(tag)管理;由於標籤是使用者對各個資料語意或概念上的描述,因此以標籤建立使用者描述可瞭解各使用者個人化的觀點與感興趣的主題。
目前計算使用者描述之間的相似度方法皆只考慮兩個使用者描述中共有的屬性。以標籤式使用者描述(tag-based user profile)為例,計算相似度時只考慮相同的標籤,字面上不同的標籤則會忽略不計。但是即使兩個標籤不同,以人類的知識會覺得它們之間具有語意相似度(semantic similarity)。因此在本論文中,我們將語意帶進標籤式使用者描述擴展成賦有語意的標籤式使用者描述(semantic tag-based user profile),接著我們訂定衡量賦有語意的標籤式使用者描述之間的相似度方法。 我們的實驗資料來自於Delicious,它是目前資料量最豐富的社群書籤網站。我們共使用20,578位使用者以及80,000個網頁的資料來衡量我們提出的方法的效能。藉由研究上常用的評估方法以及我們設計的使用者調查,兩者皆顯示我們的方法較原本的標籤式使用者描述好。 | zh_TW |
dc.description.abstract | With the rapidly growing amount of information, especially in the era of Web 2.0, users experience the problem of information overload. Based on an accurate user profile, we can eliminate unwanted items and recommend the items to the user who interests. Though user profiles have been stuidied for a long time, constructing profiles based on tags is a new research topic which emerges in recent three years. Utilizing a user's set of tags to profile the user is reasonable because tagging associates an object with a set of words which represent the semantic concepts activated by the object from the user's perspective.
Nowadays, Common similarity measures between profiles just consider the same attributes only. But two tags may have semantic similarity even if they are not the same tag. In this thesis, we propose semantic tag-based profiles to enrich profiles based on tag concepts we proposed. Each tag concept is built from a core tag which connects other tags holding similar semantic meanings with the core tag. Furthermore, we propose an adaptive similarity measure for semantic tag-based profiles which integrates semantic similarity between tags. Our evaluation is based on the data set crawled from Delicious, which is the most popular social bookmarking web site. The data set contains 20,578 users and 80,000 bookmarks after filtering the crawled data. From the results by empirical evaluation and user study, we show semantic tag-based profiles are better than tag-based profiles. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T20:11:48Z (GMT). No. of bitstreams: 1 ntu-98-R96922008-1.pdf: 1176491 bytes, checksum: bafb0f54378ffcc4abe47a2905e2220b (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | Acknowledgments iii
Abstract v List of Figures xii List of Tables xiv Chapter 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 2 Related Work 5 2.1 Social Tagging Systems . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Folksonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.3 Usage Patterns . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Semantic Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.2 WordNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.3 ConceptNet . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.4 Web-based Approaches . . . . . . . . . . . . . . . . . . . . . 22 2.3 User Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.1 Demographic User Profile . . . . . . . . . . . . . . . . . . . 24 2.3.2 Tag-based User Profile . . . . . . . . . . . . . . . . . . . . . 24 2.4 Common Similarity Measures . . . . . . . . . . . . . . . . . . . . . 27 2.4.1 Jaccard Coefficient . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.2 Cosine Similarity . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4.3 Adjusted Cosine Similarity . . . . . . . . . . . . . . . . . . . 28 2.4.4 Correlation-based Similarity . . . . . . . . . . . . . . . . . . 29 Chapter 3 Semantic Similarity Measure for Tag-based User Profiles 31 3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.1.1 Tag-based User Profile . . . . . . . . . . . . . . . . . . . . . 32 3.1.2 Semantic Similarity . . . . . . . . . . . . . . . . . . . . . . . 33 3.2 Semantic Similarity between Tag-based User Profiles . . . . . . . . . 37 3.3 Proposed Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.1 Semantic Tag-based User Profile . . . . . . . . . . . . . . . . 38 3.3.2 Similarity Measure for Semantic Tag-based User Profiles . . . 39 Chapter 4 Methodology of Semantic Tag-Based User Profiles 41 4.1 Similarity Measure for Tag-based User Profiles . . . . . . . . . . . . 42 4.2 Semantic Tag-based User Profile . . . . . . . . . . . . . . . . . . . . 43 4.3 Similarity Measure for Smantic Tag-based User Profiles . . . . . . . . 47 4.3.1 Property of the Similarity Measure . . . . . . . . . . . . . . . 50 Chapter 5 Experiment and Evaluation 53 5.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.2 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2.1 Data Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2.2 Tag Coverages in Semantic Resources . . . . . . . . . . . . . 57 5.2.3 Ratios of User’s Tag Frequencies to Total Tag Frequency . . . 60 5.3 Example Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 5.4 Empirical Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.1 Precision-Recall Graph . . . . . . . . . . . . . . . . . . . . . 63 5.4.2 Rank Accuracy Measures . . . . . . . . . . . . . . . . . . . 65 5.5 User Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.5.1 User Study Design . . . . . . . . . . . . . . . . . . . . . . . 69 5.5.2 User Study Result . . . . . . . . . . . . . . . . . . . . . . . 70 Chapter 6 Conclusion 73 6.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . 74 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Bibliography 76 | |
dc.language.iso | en | |
dc.title | 標籤式使用者描述之語意關聯相似度研究 | zh_TW |
dc.title | A Comparative Study of Semantic Similarity of Tag-based Profiles | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 許永真(Jane Yung-jen Hsu) | |
dc.contributor.oralexamcommittee | 林守德(Shou-De Lin),蔡宗翰(Tzong-Han Tsai) | |
dc.subject.keyword | 標籤,使用者描述,語意,語意相似度, | zh_TW |
dc.subject.keyword | tagging,profile,semantic,semantic similarity, | en |
dc.relation.page | 80 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2009-07-27 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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