Matches in ScholarlyData for { ?s ?p ?o. }
- 516 abstract "Web-based communities have become an important place for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.".
- 516 hasAuthorList authorList.
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- 516 title "Expertise Networks in Online Communities: Structure and Algorithms".
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- item-1 hasContent jun-zhang.
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- 520 creator brian-bershad.
- 520 creator daniel-weld.
- 520 creator eytan-adar.
- 520 creator steven-d-gribble.
- 520 type InProceedings.
- 520 label "Why We Search? Visualizing and Predicting User Behavior".
- 520 sameAs 520.
- 520 abstract "The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper, we take a first step at achieving this goal. We present a large scale study correlating the behaviors of Internet users on multiple systems ranging in size from 27 million queries to 14 million blog posts to 20,000 news articles. We formalize a model for events in these time-varying datasets and study their correlation. We have created an interface for analyzing the datasets, which includes a novel visual artifact, the DTWRadar, for summarizing differences between time series. Using our tool we identify a number of behavioral properties that allow us to understand the predictive power of patterns of use.".
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- 520 title "Why We Search? Visualizing and Predicting User Behavior".
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- item-1 hasContent eytan-adar.
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- 530 creator bernard-renger.
- 530 creator bin-wei.
- 530 creator david-gibbon.
- 530 creator giuseppe-di-fabbrizio.
- 530 creator rittwik-jana.
- 530 creator serban-jora.
- 530 creator yih-farn-chen.
- 530 type InProceedings.
- 530 label "GeoTracker: Geospatial and Temporal RSS Navigation".
- 530 sameAs 530.
- 530 abstract "The Web is rapidly moving towards a platform for mass collaboration in content production and consumption. Fresh content on a variety of topics, people, and places is being created and made available on the Web at breathtaking speed. Navigating the content effectively not only requires techniques such as aggregating various RSS-enabled feeds, but it also demands a new browsing paradigm. In this paper, we present novel geospatial and temporal browsing techniques that provide users with the capability of aggregating and navigating RSS enabled content in a timely, personalized and automatic manner. In particular, we describe a system called GeoTracker that utilizes both a geospatial representation and a temporal (chronological) presentation to help users spot the most relevant updates quickly. Within the context of this work, we provide a middleware engine that supports intelligent aggregation and dissemination of RSS feeds with personalization to desktops and mobile devices. We study the navigation capabilities of this system on two kinds of data sets, namely, 2006 World Cup soccer data collected over two months and breaking news items that occur every day. We also demonstrate that the application of such technologies to the video search results returned by YouTube and Google greatly enhances a user's ability in locating and browsing videos based on his or her geographical interests. Finally, we demonstrate that the location inference performance of GeoTracker compares well against machine learning techniques used in the natural language processing/information retrieval community. Despite its algorithm simplicity, it preserves high recall percentages.".
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- 530 title "GeoTracker: Geospatial and Temporal RSS Navigation".
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