21,915 research outputs found
Understanding Citizen Reactions and Ebola-Related Information Propagation on Social Media
In severe outbreaks such as Ebola, bird flu and SARS, people share news, and
their thoughts and responses regarding the outbreaks on social media.
Understanding how people perceive the severe outbreaks, what their responses
are, and what factors affect these responses become important. In this paper,
we conduct a comprehensive study of understanding and mining the spread of
Ebola-related information on social media. In particular, we (i) conduct a
large-scale data-driven analysis of geotagged social media messages to
understand citizen reactions regarding Ebola; (ii) build information
propagation models which measure locality of information; and (iii) analyze
spatial, temporal and social properties of Ebola-related information. Our work
provides new insights into Ebola outbreak by understanding citizen reactions
and topic-based information propagation, as well as providing a foundation for
analysis and response of future public health crises.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM 2016
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word
embedding models in recommendation, we propose a novel Regularized
Multi-Embedding (RME) based recommendation model that simultaneously
encapsulates the following ideas via decomposition: (1) which items a user
likes, (2) which two users co-like the same items, (3) which two items users
often co-liked, and (4) which two items users often co-disliked. In
experimental validation, the RME outperforms competing state-of-the-art models
in both explicit and implicit feedback datasets, significantly improving
Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition,
under the cold-start scenario for users with the lowest number of interactions,
against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in
MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source
code are available at: https://github.com/thanhdtran/RME.git.Comment: CIKM 201
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models
Public Pensions and Capital Accumulation: The Case of Brazil
We use an OLG model to study the effects of the generous public sector pension system in Brazil. In our model there are two types of workers, one working in the private sector, the other working in the public sector. Public workers produce infrastructure or education services. We find that reducing generosity of the public sector pensions has large effects on capital accumulation and steady state income.pension reform, capital accumulation
Public Sector Pension Policies and Capital Accumulation in Emerging Economies
In many emerging economies pension programs of public sector workers are more generous than pension programs of private sector workers. In this paper we investigate public pension reforms that improve efficiency and welfare by reallocating government resources from non-productive public pensions to productive public education and infrastructure investments. We argue that the opportunity costs of running generous public pension schemes for civil servants are potentially large in emerging economies that often suffer from low public investments in education and infrastructure. In addition, we quantitfy the savings distortions as well as the tax distortions from running a generous public pension program. Calculating transitions to the post-reform steady state, we find that welfare losses for the generation born before the reform are offset by welfare gains by the generations born after the reform.Social Security Reform; Generous Public Sector Pensions; Capital Accumulation; Public Education and Infrastructure Investments
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