Online Health Recommendation System: A Social Support Perspective

Online Health Communities (OHC) aim to support patients through offer them the opportunities to exchange support with others. However, patients have difficulties and problems locating expertise within the online health communities. In this regard, this study aims to create a patient recommender system to help users locate those with relevant experience and similar health status. Specifically, we aim to leverage the type of online social support users seek to determine the patient health status to build a patient status prediction model. Building the model will help create a peer recommendation system for online support group members to easily locate peers and build a sustainable online health community. Building this type of recommendation system will help patients to effectively interact with other patients who have same health status. Moreover, it will help online health communities in improving the services provided, which in turn will be reflected positively on patient’s health status

A Comparison of Evaluation Networks and Collaboration Networks in Open Source Software Communities

The open source software (OSS) development communities have experienced rapid growth in recent years. Previous social network studies on OSS communities focused on collaboration relationships. However, information about how OSS community members perceive each other is largely ignored. In this study, we report an empirical investigation of the evaluation network in an online OSS community which includes over 11,800 OSS projects and more than 94,330 developers. A collaboration network is modeled from this data set and analyzed for comparison purposes. We find the evaluation network is significantly different from collaboration network in average degree, average path length and fragmentation rate. Furthermore, we argue that the evaluation networks can be used to locate expertise - skillful developers in OSS communities and capture important social relationships among the developers missed in the collaboration network. These characteristics of the evaluation network may benefit the research of OSS development communities and expert recommendation systems

Mining and Analyzing the Academic Network

Social Network research has attracted the interests of many researchers, not only in analyzing the online social networking applications, such as Facebook and Twitter, but also in providing comprehensive services in scientific research domain. We define an Academic Network as a social network which integrates scientific factors, such as authors, papers, affiliations, publishing venues, and their relationships, such as co-authorship among authors and citations among papers. By mining and analyzing the academic network, we can provide users comprehensive services as searching for research experts, published papers, conferences, as well as detecting research communities or the evolutions hot research topics. We can also provide recommendations to users on with whom to collaborate, whom to cite and where to submit.In this dissertation, we investigate two main tasks that have fundamental applications in the academic network research. In the first, we address the problem of expertise retrieval, also known as expert finding or ranking, in which we identify and return a ranked list of researchers, based upon their estimated expertise or reputation, to user-specified queries. In the second, we address the problem of research action recommendation (prediction), specifically, the tasks of publishing venue recommendation, citation recommendation and coauthor recommendation. For both tasks, to effectively mine and integrate heterogeneous information and therefore develop well-functioning ranking or recommender systems is our principal goal. For the task of expertise retrieval, we first proposed or applied three modified versions of PageRank-like algorithms into citation network analysis; we then proposed an enhanced author-topic model by simultaneously modeling citation and publishing venue information; we finally incorporated the pair-wise learning-to-rank algorithm into traditional topic modeling process, and further improved the model by integrating groups of author-specific features. For the task of research action recommendation, we first proposed an improved neighborhood-based collaborative filtering approach for publishing venue recommendation; we then applied our proposed enhanced author-topic model and demonstrated its effectiveness in both cited author prediction and publishing venue prediction; finally we proposed an extended latent factor model that can jointly model several relations in an academic environment in a unified way and verified its performance in four recommendation tasks: the recommendation on author-co-authorship, author-paper citation, paper-paper citation and paper-venue submission. Extensive experiments conducted on large-scale real-world data sets demonstrated the superiority of our proposed models over other existing state-of-the-art methods

Government Transparency: Six Strategies for More Open and Participatory Government

Offers strategies for realizing Knight's 2009 call for e-government and openness using Web 2.0 and 3.0 technologies, including public-private partnerships to develop applications, flexible procurement procedures, and better community broadband access

The National Dialogue on the Quadrennial Homeland Security Review

Six years after its creation, the Department of Homeland Security (DHS) undertook the first Quadrennial Homeland Security Review (QHSR) to inform the design and implementation of actions to ensure the safety of the United States and its citizens. This review, mandated by the Implementing the 9/11 Commission Recommendations Act of 2007, represents the first comprehensive examination of the homeland security strategy of the nation. The QHSR includes recommendations addressing the long-term strategy and priorities of the nation for homeland security and guidance on the programs, assets, capabilities, budget, policies, and authorities of the department.Rather than set policy internally and implement it in a top-down fashion, DHS undertook the QHSR in a new and innovative way by engaging tens of thousands of stakeholders and soliciting their ideas and comments at the outset of the process. Through a series of three-week-long, web-based discussions, stakeholders reviewed materials developed by DHS study groups, submitted and discussed their own ideas and priorities, and rated or "tagged" others' feedback to surface the most relevant ideas and important themes deserving further consideration.Key FindingsThe recommendations included: (1) DHS should enhance its capacity for coordinating stakeholder engagement and consultation efforts across its component agencies, (2) DHS and other agencies should create special procurement and contracting guidance for acquisitions that involve creating or hosting such web-based engagement platforms as the National Dialogue, and (3) DHS should begin future stakeholder engagements by crafting quantitative metrics or indicators to measure such outcomes as transparency, community-building, and capacity

From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews

Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it -- once they are ready. The same is true for beers, wines, gourmet foods -- or any products where users have acquired tastes: the `best' products may not be the most `accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products -- in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.Comment: 11 pages, 7 figure

Strengthening America's Best Idea: An Independent Review of the National Park Service's Natural Resource Stewardship and Science Directorate

NRSS requested that an independent panel of the National Academy conduct a review of its effectiveness in five core functions, its relationships with key internal stakeholders, and its performance measurement system. Among other things, the National Park Service's Natural Resource Stewardship and Science Directorate (NRSS) is responsible for providing usable natural and social science information throughout the National Park Service (NPS). NRSS leadership requested this review of the directorate's performance on five core functions, its relationships with key internal NPS stakeholders, and its performance measurement system.Main FindingsThe panel determined that NRSS is a highly regarded organization that provides independent, credible scientific expertise and technical information. The panel also found that NRSS and NPS have additional opportunities to advance natural resource stewardship throughout the Service. If implemented, the panel's eight major recommendations will: (1) help the Service respond to the parks' environmental challenges while raising public awareness about the condition of these special places; (2) strengthen NRSS as an organization; (3) promote scientifically based decision-making at the national, regional, and park levels; and (4) improve the existing performance measurement system

Accurator: Nichesourcing for Cultural Heritage

With more and more cultural heritage data being published online, their usefulness in this open context depends on the quality and diversity of descriptive metadata for collection objects. In many cases, existing metadata is not adequate for a variety of retrieval and research tasks and more specific annotations are necessary. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used for eliciting simple annotations, identifying people with the required expertise might prove troublesome for tasks requiring more complex or domain-specific knowledge. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is threefold: 1) a nichesourcing methodology, 2) an annotation tool for experts and 3) validation of the methodology and tool in three case studies. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high quality annotations in a variety of domains and annotation tasks. A user evaluation indicates the tool is suited and usable for domain specific annotation tasks