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Register a new userRecommending research articles to consumers of online vaccination information
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Added by
Adam G. Dunn
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Online health communications often provide biased interpretations of evidence and have unreliable links to the source research. We tested the feasibility of a tool for matching webpages to their source evidence. From 207,538 eligible vaccination-related PubMed articles, we evaluated several approaches using 3,573 unique links to webpages from Altmetric. We evaluated methods for ranking the source articles for vaccine-related research described on webpages, comparing simple baseline feature representation and dimensionality reduction approaches to those augmented with canonical correlation analysis (CCA). Performance measures included the median rank of the correct source article; the percentage of webpages for which the source article was correctly ranked first (recall@1); and the percentage ranked within the top 50 candidate articles (recall@50). While augmenting baseline methods using CCA generally improved results, no CCA-based approach outperformed a baseline method, which ranked the correct source article first for over one quarter of webpages and in the top 50 for more than half. Tools to help people identify evidence-based sources for the content they access on vaccination-related webpages are potentially feasible and may support the prevention of bias and misrepresentation of research in news and social media.
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Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.
We analyze access statistics of a hundred and fifty blog entries and news articles, for periods of up to three years. Access rate falls as an inverse power of time passed since publication. The power law holds for periods of up to thousand days. The exponents are different for different blogs and are distributed between 0.6 and 3.2. We argue that the decay of attention to a web article is caused by the link to it first dropping down the list of links on the websites front page, and then disappearing from the front page and its subsequent movement further into background. The other proposed explanations that use a decaying with time novelty factor, or some intricate theory of human dynamics cannot explain all of the experimental observations.
Automated classification of metadata of research data by their discipline(s) of research can be used in scientometric research, by repository service providers, and in the context of research data aggregation services. Openly available metadata of the DataCite index for research data were used to compile a large training and evaluation set comprised of 609,524 records, which is published alongside this paper. These data allow to reproducibly assess classification approaches, such as tree-based models and neural networks. According to our experiments with 20 base classes (multi-label classification), multi-layer perceptron models perform best with a f1-macro score of 0.760 closely followed by Long Short-Term Memory models (f1-macro score of 0.755). A possible application of the trained classification models is the quantitative analysis of trends towards interdisciplinarity of digital scholarly output or the characterization of growth patterns of research data, stratified by discipline of research. Both applications perform at scale with the proposed models which are available for re-use.
With the advancement in the technology sector spanning over every field, a huge influx of information is inevitable. Among all the opportunities that the advancements in the technology have brought, one of them is to propose efficient solutions for data retrieval. This means that from an enormous pile of data, the retrieval methods should allow the users to fetch the relevant and recent data over time. In the field of entertainment and e-commerce, recommender systems have been functioning to provide the aforementioned. Employing the same systems in the medical domain could definitely prove to be useful in variety of ways. Following this context, the goal of this paper is to propose collaborative filtering based recommender system in the healthcare sector to recommend remedies based on the symptoms experienced by the patients. Furthermore, a new dataset is developed consisting of remedies concerning various diseases to address the limited availability of the data. The proposed recommender system accepts the prognostic markers of a patient as the input and generates the best remedy course. With several experimental trials, the proposed model achieved promising results in recommending the possible remedy for given prognostic markers.
Recommender systems have played a vital role in online platforms due to the ability of incorporating users personal tastes. Beyond accuracy, diversity has been recognized as a key factor in recommendation to broaden users horizons as well as to promote enterprises sales. However, the trading-off between accuracy and diversity remains to be a big challenge, and the data and user biases have not been explored yet. In this paper, we develop an adaptive learning framework for accurate and diversified recommendation. We generalize recent proposed bi-lateral branch network in the computer vision community from image classification to item recommendation. Specifically, we encode domain level diversity by adaptively balancing accurate recommendation in the conventional branch and diversified recommendation in the adaptive branch of a bilateral branch network. We also capture user level diversity using a two-way adaptive metric learning backbone network in each branch. We conduct extensive experiments on three real-world datasets. Results demonstrate that our proposed approach consistently outperforms the state-of-the-art baselines.
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