8,372 research outputs found
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social
networks and in the rapidly evolving parallel-field of network science.
Although several instances of social network analysis in health services
research have appeared recently, the majority involve only the most basic
methods and thus scratch the surface of what might be accomplished.
Cutting-edge methods using relevant examples and illustrations in health
services research are provided
Inferring Degree Of Localization Of Twitter Persons And Topics Through Time, Language, And Location Features
Identifying authoritative influencers related to a geographic area (geo-influencers) can aid content recommendation systems and local expert finding. This thesis addresses this important problem using Twitter data.
A geo-influencer is identified via the locations of its followers. On Twitter, due to privacy reasons, the location reported by followers is limited to profile via a textual string or messages with coordinates. However, this textual string is often not possible to geocode and less than 1\% of message traffic provides coordinates. First, the error rates associated with Google\u27s geocoder are studied and a classifier is built that gives a warning for self-reported locations that are likely incorrect. Second, it is shown that city-level geo-influencers can be identified without geocoding by leveraging the power of Google search and follower-followee network structure. Third, we illustrate that the global vs. local influencer, at the timezone level, can be identified using a classifier using the temporal features of the followers. For global influencers, spatiotemporal analysis helps understand the evolution of their popularity over time. When applied over message traffic, the approach can differentiate top trending topics and persons in different geographical regions. Fourth, we constrain a timezone to a set of possible countries and use language features for training a high-level geocoder to further localize an influencer\u27s geographic area. Finally, we provide a repository of geo-influencers for applications related to content recommendation. The repository can be used for filtering influencers based on their audience\u27s demographics related to location, time, language, gender, and ethnicity
Key Factors Supporting Small-Scale Coastal Fisheries Management
This synthesis was designed to provide an evidence base on the success factors in small-scale coastal fisheries management in developing countries and, in turn, to assist the Rockefeller Foundation in developing its strategy for its Oceans and Fisheries Initiative. In doing so, it identifies and describes some 20 key factors believed to influence success in small-scale coastal fisheries management. The report was completed via a rapid review of key sources of knowledge from formal published literature, institutional literature, key informants and Internet searches. The focus was on key success factors in achieving a balance of social, economic and ecological benefits from the management of small-scale coastal fisheries. A summary of these success factors can also be explored via an interactive visualization that accompanies this report
Influence Maximization in Social Networks: A Survey
Online social networks have become an important platform for people to
communicate, share knowledge and disseminate information. Given the widespread
usage of social media, individuals' ideas, preferences and behavior are often
influenced by their peers or friends in the social networks that they
participate in. Since the last decade, influence maximization (IM) problem has
been extensively adopted to model the diffusion of innovations and ideas. The
purpose of IM is to select a set of k seed nodes who can influence the most
individuals in the network.
In this survey, we present a systematical study over the researches and
future directions with respect to IM problem. We review the information
diffusion models and analyze a variety of algorithms for the classic IM
algorithms. We propose a taxonomy for potential readers to understand the key
techniques and challenges. We also organize the milestone works in time order
such that the readers of this survey can experience the research roadmap in
this field. Moreover, we also categorize other application-oriented IM studies
and correspondingly study each of them. What's more, we list a series of open
questions as the future directions for IM-related researches, where a potential
reader of this survey can easily observe what should be done next in this
field
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