17,756 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
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A Dose Relationship Between Brain Functional Connectivity and Cumulative Head Impact Exposure in Collegiate Water Polo Players.
A growing body of evidence suggests that chronic, sport-related head impact exposure can impair brain functional integration and brain structure and function. Evidence of a robust inverse relationship between the frequency and magnitude of repeated head impacts and disturbed brain network function is needed to strengthen an argument for causality. In pursuing such a relationship, we used cap-worn inertial sensors to measure the frequency and magnitude of head impacts sustained by eighteen intercollegiate water polo athletes monitored over a single season of play. Participants were evaluated before and after the season using computerized cognitive tests of inhibitory control and resting electroencephalography. Greater head impact exposure was associated with increased phase synchrony [r (16) > 0.626, p < 0.03 corrected], global efficiency [r (16) > 0.601, p < 0.04 corrected], and mean clustering coefficient [r (16) > 0.625, p < 0.03 corrected] in the functional networks formed by slow-wave (delta, theta) oscillations. Head impact exposure was not associated with changes in performance on the inhibitory control tasks. However, those with the greatest impact exposure showed an association between changes in resting-state connectivity and a dissociation between performance on the tasks after the season [r (16) = 0.481, p = 0.043] that could also be attributed to increased slow-wave synchrony [F (4, 135) = 113.546, p < 0.001]. Collectively, our results suggest that athletes sustaining the greatest head impact exposure exhibited changes in whole-brain functional connectivity that were associated with altered information processing and inhibitory control
A hierarchical Bayesian model for predicting ecological interactions using scaled evolutionary relationships
Identifying undocumented or potential future interactions among species is a
challenge facing modern ecologists. Recent link prediction methods rely on
trait data, however large species interaction databases are typically sparse
and covariates are limited to only a fraction of species. On the other hand,
evolutionary relationships, encoded as phylogenetic trees, can act as proxies
for underlying traits and historical patterns of parasite sharing among hosts.
We show that using a network-based conditional model, phylogenetic information
provides strong predictive power in a recently published global database of
host-parasite interactions. By scaling the phylogeny using an evolutionary
model, our method allows for biological interpretation often missing from
latent variable models. To further improve on the phylogeny-only model, we
combine a hierarchical Bayesian latent score framework for bipartite graphs
that accounts for the number of interactions per species with the host
dependence informed by phylogeny. Combining the two information sources yields
significant improvement in predictive accuracy over each of the submodels
alone. As many interaction networks are constructed from presence-only data, we
extend the model by integrating a correction mechanism for missing
interactions, which proves valuable in reducing uncertainty in unobserved
interactions.Comment: To appear in the Annals of Applied Statistic
Network Structure Explains the Impact of Attitudes on Voting Decisions
Attitudes can have a profound impact on socially relevant behaviours, such as
voting. However, this effect is not uniform across situations or individuals,
and it is at present difficult to predict whether attitudes will predict
behaviour in any given circumstance. Using a network model, we demonstrate that
(a) more strongly connected attitude networks have a stronger impact on
behaviour, and (b) within any given attitude network, the most central attitude
elements have the strongest impact. We test these hypotheses using data on
voting and attitudes toward presidential candidates in the US presidential
elections from 1980 to 2012. These analyses confirm that the predictive value
of attitude networks depends almost entirely on their level of connectivity,
with more central attitude elements having stronger impact. The impact of
attitudes on voting behaviour can thus be reliably determined before elections
take place by using network analyses.Comment: Final version published in Scientific Report
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