852 research outputs found
Evolution of Ego-networks in Social Media with Link Recommendations
Ego-networks are fundamental structures in social graphs, yet the process of
their evolution is still widely unexplored. In an online context, a key
question is how link recommender systems may skew the growth of these networks,
possibly restraining diversity. To shed light on this matter, we analyze the
complete temporal evolution of 170M ego-networks extracted from Flickr and
Tumblr, comparing links that are created spontaneously with those that have
been algorithmically recommended. We find that the evolution of ego-networks is
bursty, community-driven, and characterized by subsequent phases of explosive
diameter increase, slight shrinking, and stabilization. Recommendations favor
popular and well-connected nodes, limiting the diameter expansion. With a
matching experiment aimed at detecting causal relationships from observational
data, we find that the bias introduced by the recommendations fosters global
diversity in the process of neighbor selection. Last, with two link prediction
experiments, we show how insights from our analysis can be used to improve the
effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl
Beautiful and damned. Combined effect of content quality and social ties on user engagement
User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on
Knowledge and Data Engineering (Volume: PP, Issue: 99
Layered evaluation of interactive adaptive systems : framework and formative methods
Peer reviewedPostprin
Towards a Reference Architecture for Female-Sensitive Drug Management
Due to various biological factors, males and females differ in their response to drug treatment. However, there is still a lack of knowledge of the effects resulting from sex-differences in the medical field, especially due to the issue of underrepresentation of females in clinical studies. Considering severe diseases that are related to the cardiovascular system, which are likely to be perilous, counteracting this lack and emphasizing the need for sex-dependent drug treatment is of high importance. Thus, this research-in-progress paper aims at strengthening the female perspective in drug management by proposing design considerations on IS regarding recommender systems in healthcare for reinforcing shared decision-making and person-centered care. The resulting artefact presented will be a reference architecture with a mobile application as the interface to patients and healthcare professionals as well as a data- driven backend to collect and process data on sex specificity in the medical treatment of cardiovascular diseases (CVD)
Poincare: Recommending Publication Venues via Treatment Effect Estimation
Choosing a publication venue for an academic paper is a crucial step in the
research process. However, in many cases, decisions are based solely on the
experience of researchers, which often leads to suboptimal results. Although
there exist venue recommender systems for academic papers, they recommend
venues where the paper is expected to be published. In this study, we aim to
recommend publication venues from a different perspective. We estimate the
number of citations a paper will receive if the paper is published in each
venue and recommend the venue where the paper has the most potential impact.
However, there are two challenges to this task. First, a paper is published in
only one venue, and thus, we cannot observe the number of citations the paper
would receive if the paper were published in another venue. Secondly, the
contents of a paper and the publication venue are not statistically
independent; that is, there exist selection biases in choosing publication
venues. In this paper, we formulate the venue recommendation problem as a
treatment effect estimation problem. We use a bias correction method to
estimate the potential impact of choosing a publication venue effectively and
to recommend venues based on the potential impact of papers in each venue. We
highlight the effectiveness of our method using paper data from computer
science conferences.Comment: Journal of Informetric
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation
Delivering treatment recommendations via pervasive electronic devices such as
mobile phones has the potential to be a viable and scalable treatment medium
for long-term health behavior management. But active experimentation of
treatment options can be time-consuming, expensive and altogether unethical in
some cases. There is a growing interest in methodological approaches that allow
an experimenter to learn and evaluate the usefulness of a new treatment
strategy before deployment. We present the first development of a treatment
recommender system for emotion regulation using real-world historical mobile
digital data from n = 114 high socially anxious participants to test the
usefulness of new emotion regulation strategies. We explore a number of offline
contextual bandits estimators for learning and propose a general framework for
learning algorithms. Our experimentation shows that the proposed doubly robust
offline learning algorithms performed significantly better than baseline
approaches, suggesting that this type of recommender algorithm could improve
emotion regulation. Given that emotion regulation is impaired across many
mental illnesses and such a recommender algorithm could be scaled up easily,
this approach holds potential to increase access to treatment for many people.
We also share some insights that allow us to translate contextual bandit models
to this complex real-world data, including which contextual features appear to
be most important for predicting emotion regulation strategy effectiveness.Comment: Accepted at RecSys 202
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
In machine learning, exploring data correlations to predict outcomes is a
fundamental task. Recognizing causal relationships embedded within data is
pivotal for a comprehensive understanding of system dynamics, the significance
of which is paramount in data-driven decision-making processes. Beyond
traditional methods, there has been a surge in the use of graph neural networks
(GNNs) for causal learning, given their capabilities as universal data
approximators. Thus, a thorough review of the advancements in causal learning
using GNNs is both relevant and timely. To structure this review, we introduce
a novel taxonomy that encompasses various state-of-the-art GNN methods employed
in studying causality. GNNs are further categorized based on their applications
in the causality domain. We further provide an exhaustive compilation of
datasets integral to causal learning with GNNs to serve as a resource for
practical study. This review also touches upon the application of causal
learning across diverse sectors. We conclude the review with insights into
potential challenges and promising avenues for future exploration in this
rapidly evolving field of machine learning
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