11,061 research outputs found
The Price of Fog: a Data-Driven Study on Caching Architectures in Vehicular Networks
Vehicular users are expected to consume large amounts of data, for both
entertainment and navigation purposes. This will put a strain on cellular
networks, which will be able to cope with such a load only if proper caching is
in place, this in turn begs the question of which caching architecture is the
best-suited to deal with vehicular content consumption. In this paper, we
leverage a large-scale, crowd-collected trace to (i) characterize the vehicular
traffic demand, in terms of overall magnitude and content breakup, (ii) assess
how different caching approaches perform against such a real-world load, (iii)
study the effect of recommendation systems and local contents. We define a
price-of-fog metric, expressing the additional caching capacity to deploy when
moving from traditional, centralized caching architectures to a "fog computing"
approach, where caches are closer to the network edge. We find that for
location-specific contents, such as the ones that vehicular users are most
likely to request, such a price almost disappears. Vehicular networks thus make
a strong case for the adoption of mobile-edge caching, as we are able to reap
the benefit thereof -- including a reduction in the distance traveled by data,
within the core network -- with little or no of the associated disadvantages.Comment: ACM IoV-VoI 2016 MobiHoc Workshop, The 17th ACM International
Symposium on Mobile Ad Hoc Networking and Computing: MobiHoc 2016-IoV-VoI
Workshop, Paderborn, German
Local Ranking Problem on the BrowseGraph
The "Local Ranking Problem" (LRP) is related to the computation of a
centrality-like rank on a local graph, where the scores of the nodes could
significantly differ from the ones computed on the global graph. Previous work
has studied LRP on the hyperlink graph but never on the BrowseGraph, namely a
graph where nodes are webpages and edges are browsing transitions. Recently,
this graph has received more and more attention in many different tasks such as
ranking, prediction and recommendation. However, a web-server has only the
browsing traffic performed on its pages (local BrowseGraph) and, as a
consequence, the local computation can lead to estimation errors, which hinders
the increasing number of applications in the state of the art. Also, although
the divergence between the local and global ranks has been measured, the
possibility of estimating such divergence using only local knowledge has been
mainly overlooked. These aspects are of great interest for online service
providers who want to: (i) gauge their ability to correctly assess the
importance of their resources only based on their local knowledge, and (ii)
take into account real user browsing fluxes that better capture the actual user
interest than the static hyperlink network. We study the LRP problem on a
BrowseGraph from a large news provider, considering as subgraphs the
aggregations of browsing traces of users coming from different domains. We show
that the distance between rankings can be accurately predicted based only on
structural information of the local graph, being able to achieve an average
rank correlation as high as 0.8
Achieving Green and Healthy Homes and Communities in America
In the Fall of 2010, the National Coalition to End Childhood Lead Poisioning contracted with the National Academy to develop and execute an online dialogue that would examine ways to increase the health, safety, and energy efficiency of low- to moderate-income homes. Since 1999, the National Coalition had worked to improve low- to moderate-income housing through the support and execution of home interventions that addressed multiple issues within a home at one time; an approach that often did not align with other traditional, single-issue housing assistance programs. By 2010, the National Coalition had taken on the leadership of the Green and Healthy Homes Initiative, a public-private partnership focused on integrating funding streams to improve low- to middle-income homes across the country.With plans to expand the GHHI's operations, the National Coalition partnered with the National Academy to conduct the National Dialogue on Green and Healthy Homes, a collaborative online dailogue in which participants were asked to identify challenges to, and innovative practices for, improving the health, safety and energy-efficiency of low- to moderate- income homes. The Dialogue was live from November 4-November 22, 2010, and collected 100 hundred ideas and 362 comments from 320 registered users. Over the course of its two and a half week duration, the Dialogue received more than 2,500 visits from over 1,100 people in 48 states and territories. Key FindingsBy reviewing the feedback received in the Dialogue, the Panel was able to make a number of recommendations on how the green and healthy homes community of practice could increase the health, safety and energy efficiency of homes across the country. These recommendations included: Conduct an evaluation of current housing standards to determine if they meet the Nation's health, safety, and energy efficiency needs; Develop a tiered performance standard for healthy, safe and energy efficient homes; Group government funding streams to better align programs with the comprehensive intervention approach; Develop a long-term funding strategy to support efforts after Recovery Act funding ends; and Educate government decisionmakers and the public on the importance of developing green and healthy homes and communities, and the work that supports that development
Of course we share! Testing Assumptions about Social Tagging Systems
Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior
Countering Personalized Speech
Social media platforms use personalization algorithms to make content curation decisions for each end user. These personalized recommendation decisions are essentially speech conveying a platform\u27s predictions on content relevance for each end user. Yet, they are causing some of the worst problems on the internet. First, they facilitate the precipitous spread of mis- and disinformation by exploiting the very same biases and insecurities that drive end user engagement with such content. Second, they exacerbate social media addiction and related mental health harms by leveraging users\u27 affective needs to drive engagement to greater and greater heights. Lastly, they erode end user privacy and autonomy as both sources and incentives for data collection.
As with any harmful speech, the solution is often counterspeech. Free speech jurisprudence considers counterspeech the most speech-protective weapon to combat false or harmful speech. Thus, to combat problematic recommendation decisions, social media platforms, policymakers, and other stakeholders should embolden end users to use counterspeech to reduce the harmful effects of platform personalization.
One way to implement this solution is through end user personalization inputs. These inputs reflect end user expression about a platform\u27s recommendation decisions. However, industry-standard personalization inputs are failing to provide effective countermeasures against problematic recommendation decisions. On most, if not all, major social media platforms, the existing inputs confer limited ex post control over the platform\u27s recommendation decisions. In order for end user personalization to achieve the promise of counterspeech, I make several proposals along key regulatory modalities, including revising the architecture of personalization inputs to confer robust ex ante capabilities that filter by content type and characteristics
Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
Recommender systems leverage both content and user interactions to generate
recommendations that fit users' preferences. The recent surge of interest in
deep learning presents new opportunities for exploiting these two sources of
information. To recommend items we propose to first learn a user-independent
high-dimensional semantic space in which items are positioned according to
their substitutability, and then learn a user-specific transformation function
to transform this space into a ranking according to the user's past
preferences. An advantage of the proposed architecture is that it can be used
to effectively recommend items using either content that describes the items or
user-item ratings. We show that this approach significantly outperforms
state-of-the-art recommender systems on the MovieLens 1M dataset.Comment: 6 pages, RecSys 2016 RSDL worksho
The Limits of Popularity-Based Recommendations, and the Role of Social Ties
In this paper we introduce a mathematical model that captures some of the
salient features of recommender systems that are based on popularity and that
try to exploit social ties among the users. We show that, under very general
conditions, the market always converges to a steady state, for which we are
able to give an explicit form. Thanks to this we can tell rather precisely how
much a market is altered by a recommendation system, and determine the power of
users to influence others. Our theoretical results are complemented by
experiments with real world social networks showing that social graphs prevent
large market distortions in spite of the presence of highly influential users.Comment: 10 pages, 9 figures, KDD 201
- …