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- item-1 hasContent jae-wook-ahn.
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- 801 creator mikhail-bilenko.
- 801 creator ryen-white.
- 801 type InProceedings.
- 801 label "Mining the Search Trails of Surfing Crowds: Identifying Relevant Websites From User Activity".
- 801 sameAs 801.
- 801 abstract "The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that user interactions with search engines can be employed to improve document ranking, browsing behavior that occurs beyond search result pages has been largely overlooked in prior work. The paper demonstrates that users' post-search browsing activity strongly reflects implicit endorsement of visited pages, which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails. We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries. Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation (e.g., clickthrough logs), and yields accuracy improvements when employed as a feature in learning to rank for Web search.".
- 801 hasAuthorList authorList.
- 801 hasTopic World_Wide_Web.
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- 801 keyword "Implicit feedback".
- 801 keyword "Learning from user behavior".
- 801 keyword "Mining search and browsing logs".
- 801 keyword "Web search".
- 801 title "Mining the Search Trails of Surfing Crowds: Identifying Relevant Websites From User Activity".
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- 803 creator eugene-agichtein.
- 803 creator hongyuan-zha.
- 803 creator jiang-bian.
- 803 creator yandong-liu.
- 803 type InProceedings.
- 803 label "Finding the Right Facts in the Crowd: Factoid Question Answering over Social Media".
- 803 sameAs 803.
- 803 abstract "Community Question Answering has emerged as a popular and effective paradigm for a wide range of information needs. For example, when precise factual information is desired, such as an obscure piece of trivia, it is now effective to simply post a question on a popular community QA site such as Yahoo! Answers, and to rely on other users to provide answers, often within minutes. The importance of such community QA sites is magnified as they create archives for millions of questions and hundreds of millions of answers, many of which are invaluable for the information needs of other searchers. However, to make this immense body of knowledge accessible, effective search and answer retrieval is required. In particular, precisely because any user can contribute to a question, the majority of the content reflects personal, often unsubstantiated opinions. A ranking that combines both relevance and quality is required to make such archives usable for factual information retrieval. This task is challenging, as the structure and the content of community QA archive differ significantly from the web. In this paper we present a general ranking framework for factual information retrieval from social media. We show that our method is significantly more accurate than existing state of the art at retrieving well-formed, factual answers to questions, as evaluated on a standard QA benchmark. We demonstrate that our learning framework can be tuned with the minimum of human labeling. Finally, we provide result analysis to gain deeper understanding of which features are significant for social media search and retrieval. Our system can be used as a crucial building block for combining results from a variety of social media content with general web search results, and to better integrate social media content for effective information access.".
- 803 hasAuthorList authorList.
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- 803 keyword "learning ranking functions".
- 803 keyword "question answering".
- 803 keyword "social media".
- 803 title "Finding the Right Facts in the Crowd: Factoid Question Answering over Social Media".
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- item-1 hasContent jiang-bian.
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- 806 creator chengxiang-zhai.
- 806 creator deng-cai.
- 806 creator duo-zhang.
- 806 creator qiaozhu-mei.
- 806 type InProceedings.
- 806 label "Topic Modeling with Network Regularization".
- 806 sameAs 806.
- 806 abstract "In this paper, we define the problem of topic modeling with network structure (TMN) and propose a novel framework which regularizes a statistical topic model based on a graph structure in the data. The proposed method bridges topic modeling and social network analysis, which leverages the power of both statistical topic models and discrete regularization. The output of this model well summarizes topics in text, maps a topic on the network, and discovers topical communities. With concrete selection of a topic model and a graph-based regularizer, our model can be applied to text mining problems such as author-topic analysis, community discovery, and spatial text mining. Empirical experiments on two different genres of data show that our approach is effective, which improves text-oriented methods as well as network-oriented methods. The proposed model is general; it can be applied to any text collections with a mixture of topics and an associated network structure.".
- 806 hasAuthorList authorList.
- 806 hasTopic World_Wide_Web.
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- 806 keyword "graph-based regularization".
- 806 keyword "social networks".
- 806 keyword "statistical topic models".
- 806 title "Topic Modeling with Network Regularization".
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