QA Document Recommendation Techniques for Knowledge Communities
博士 === 國立交通大學 === 資訊管理研究所 === 102 === With the emergence of Social Media, Social Question-Answering (SQA) websites have become common knowledge production and sharing platforms. This platform provides knowledge community services where users with common interests, needs or expertise can form a knowl...
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ndltd-TW-102NCTU53960462015-10-14T00:18:37Z http://ndltd.ncl.edu.tw/handle/56260803619245722369 QA Document Recommendation Techniques for Knowledge Communities 知識社群問答文件推薦技術 Chen, Yu-Hsuan 陳宇軒 博士 國立交通大學 資訊管理研究所 102 With the emergence of Social Media, Social Question-Answering (SQA) websites have become common knowledge production and sharing platforms. This platform provides knowledge community services where users with common interests, needs or expertise can form a knowledge community. Community members can collect and share QA knowledge (documents) regarding their interests. However, due to the massive amount of QAs created every day, information overload can become a major problem. Consequently, a recommender system is needed to suggest QA documents for communities of SQA websites. In this thesis, we propose several novel methods, called GTPR-based approaches, to recommend related QA documents for knowledge communities of SQA sites. The proposed methods recommend QA documents by considering community-specific features, the relationships between knowledge documents, and documents’ relevance to the communities. In addition, due to the robustness problem of the existing topic grouping method using user-defined tags in Social Media, this study further propose a novel method, called GPTLR, incorporating the community’s latent topics of interest and collection weights based on members’ topical reputations to improve content-based recommendation models. This research evaluates and compares the proposed methods using a real-world dataset collected from a SQA website. Experimental results show that the proposed methods outperform other traditional methods, providing a more effective and accurate recommendations of Q&;A documents to knowledge communities. Liu, Duen-Ren 劉敦仁 2014 學位論文 ; thesis 67 en_US |
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博士 === 國立交通大學 === 資訊管理研究所 === 102 === With the emergence of Social Media, Social Question-Answering (SQA) websites have become common knowledge production and sharing platforms. This platform provides knowledge community services where users with common interests, needs or expertise can form a knowledge community. Community members can collect and share QA knowledge (documents) regarding their interests. However, due to the massive amount of QAs created every day, information overload can become a major problem. Consequently, a recommender system is needed to suggest QA documents for communities of SQA websites.
In this thesis, we propose several novel methods, called GTPR-based approaches, to recommend related QA documents for knowledge communities of SQA sites. The proposed methods recommend QA documents by considering community-specific features, the relationships between knowledge documents, and documents’ relevance to the communities. In addition, due to the robustness problem of the existing topic grouping method using user-defined tags in Social Media, this study further propose a novel method, called GPTLR, incorporating the community’s latent topics of interest and collection weights based on members’ topical reputations to improve content-based recommendation models.
This research evaluates and compares the proposed methods using a real-world dataset collected from a SQA website. Experimental results show that the proposed methods outperform other traditional methods, providing a more effective and accurate recommendations of Q&;A documents to knowledge communities.
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author2 |
Liu, Duen-Ren |
author_facet |
Liu, Duen-Ren Chen, Yu-Hsuan 陳宇軒 |
author |
Chen, Yu-Hsuan 陳宇軒 |
spellingShingle |
Chen, Yu-Hsuan 陳宇軒 QA Document Recommendation Techniques for Knowledge Communities |
author_sort |
Chen, Yu-Hsuan |
title |
QA Document Recommendation Techniques for Knowledge Communities |
title_short |
QA Document Recommendation Techniques for Knowledge Communities |
title_full |
QA Document Recommendation Techniques for Knowledge Communities |
title_fullStr |
QA Document Recommendation Techniques for Knowledge Communities |
title_full_unstemmed |
QA Document Recommendation Techniques for Knowledge Communities |
title_sort |
qa document recommendation techniques for knowledge communities |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/56260803619245722369 |
work_keys_str_mv |
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