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- item-1 hasContent yu-ru-lin.
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- item-2 hasContent jimeng-sun.
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- 188 type InProceedings.
- 188 label "Ranking User-Created Contents by Search User's Inclination in Online Communities".
- 188 sameAs 188.
- 188 abstract "Searching posts effectively has become an important issue in large-scale online communities. Especially, if search users have different inclinations when they search posts, they have different kinds of posts in their minds. To address this problem, in this paper, we propose a scheme of ranking posts based on search users' inclination. User ranking score is employed to capture posts that are relevant to a specific user inclination. Specifically, we present a scheme to rank posts in terms of user expertise and popularity. Experimental results show that different user inclinations can produce quite different search results and the proposed scheme achieves about 70% accuracy.".
- 188 hasAuthorList authorList.
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- 188 keyword "Poster Session".
- 188 title "Ranking User-Created Contents by Search User's Inclination in Online Communities".
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- item-1 hasContent hyoseop-shin.
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- item-2 hasContent jeehoon-lee.
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- 189 type InProceedings.
- 189 label "Retaining Personal Expression for Social Search".
- 189 sameAs 189.
- 189 abstract "Web is being extensively used for personal expression, which includes ratings, reviews, recommendations, blogs. This user created content, e.g. book review on Amazon.com, becomes the property of the website, and the user often does not have easy access to it. In some cases, user's feedback may get averaged with feedback from other users e.g. ratings of a video. We argue that the creator of such content needs to be able to retain (a link to) her created content. We introduce the concept of MEB which is a user controlled store of such retained links. A MEB allows a user to access/share all the reviews she has given on different websites. With this capability users can allow their friends to search through their feedback. Searching through one's social network allows harnessing the power of social networks where known relationships provide the context & trust necessary to interpret feedback.".
- 189 hasAuthorList authorList.
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- 189 keyword "Poster Session".
- 189 title "Retaining Personal Expression for Social Search".
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- 19 type InProceedings.
- 19 label "Incorporating Site-Level Knowledge to Extract Structured Data from Web Forums".
- 19 sameAs 19.
- 19 abstract "Web forums have become an important data resource for many web applications, but extracting structured data from unstructured web forum pages is still a challenging task due to both complex page layout designs and unrestricted user created posts. In this paper, we study the problem of structured data extraction from various web forum sites. Our target is to find a solution as general as possible to extract structured data, such as post title, post author, post time, and post content from any forum site. In contrast to most existing information extraction methods, which only leverage the knowledge inside an individual page, we incorporate both page-level and site-level knowledge and employ Markov logic networks (MLNs) to effectively integrate all useful evidence by learning their importance automatically. Site-level knowledge includes (1) the linkages among different object pages, such as list pages and post pages, and (2) the interrelationships of pages belonging to the same object. The experimental results on 20 forums show a very encouraging information extraction performance, and demonstrate the ability of the proposed approach on various forums. We also show that the performance is limited if only page-level knowledge is used, while when incorporating the site-level knowledge both precision and recall can be significantly improved.".
- 19 hasAuthorList authorList.
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- 19 keyword "Data Mining".
- 19 title "Incorporating Site-Level Knowledge to Extract Structured Data from Web Forums".
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- item-1 hasContent jiang-ming-yang.
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- item-2 hasContent rui-cai.
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- item-6 hasContent wei-ying-ma.
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- 190 type InProceedings.
- 190 label "Discovering the Staring People From Social Networks".