No abstract

Online social network information promises to increase recommendation accuracy beyond the capabilities of purely rating/feedback-driven recommender systems (RS). As to better serve users' activities across different domains, many online social networks now support a new feature of "Friends Circles", which refines the domain-oblivious "Friends" concept. RS should also benefit from domain-specific "Trust Circles". Intuitively, a user may trust different subsets of friends regarding different domains. Unfortunately, in most existing multi-category rating datasets, a user's social connections from all categories are mixed together. This paper presents an effort to develop circle-based RS. We focus on inferring category-specific social trust circles from available rating data combined with social network data. We outline several variants of weighting friends within circles based on their inferred expertise levels. Through experiments on publicly available data, we demonstrate that the proposed circle-based recommendation models can better utilize user's social trust information, resulting in increased recommendation accuracy.

Recommendation accuracy can be improved by incorporating trust relationships derived from social networks. Most recent work on social network based recommendation is focused on minimizing the root mean square error (RMSE). Social network based top-k recommendation, which recommends to a user a small number of items at a time, is not well studied. In this paper, we conduct a comprehensive study on improving the accuracy of top-k recommendation using social networks. We first show that the existing social-trust enhanced Matrix Factorization (MF) models can be tailored for top-k recommendation by including observed and missing ratings in their training objective functions. We also propose a Nearest Neighbor (NN) based top-k recommendation method that combines users' neighborhoods in the trust network with their neighborhoods in the latent feature space. Experimental results on two publicly available datasets show that social networks can significantly improve the top-k hit ratio, especially for cold start users. Surprisingly, we also found that the technical approach for combining feedback data (e.g. ratings) with social network information that works best for minimizing RMSE works poorly for maximizing the hit ratio, and vice versa.

Background and objectiveMoyamoya disease (MMD) is an increasingly recognised cause of stroke, mainly described in East Asia. China is the largest nation in Asia, but few studies reported the epidemiology of MMD, especially at a national level. We aimed to estimate the incidence and prevalence of MMD in China.MethodsWe performed a population-based study using data from the national databases of Urban Basic Medical Insurance between 2013 and 2016, covering approximately 0.50 billion individuals. MMD cases were identified by diagnostic code (International Classification of Diseases, 10th Revision I67.5) or related diagnostic text.ResultsA total of 1987 MMD patients (mean age 44.45±14.30 years, female-to-male ratio 1.12) were identified, representing a national crude incidence of 0.59 (95% CI: 0.49 to 0.68) and a prevalence of 1.01 (95% CI: 0.81 to 1.21) per 100 000 person-years in 2016. Rates were higher in females than in males for the incidence (0.66 vs 0.52) and prevalence (1.05 vs 0.90). And the age-specific rates showed a bimodal distribution, with the highest peak in middle-aged group and the second peak in child group.ConclusionsOur results confirm that MMD is relatively common in East Asians, but the rates in China were lower than those in other East Asian countries such as Japan and Korea. The unique epidemiological features, including a relatively weak female predominance and a shift in the highest peak of incidence from children to adults, revealed new sight into MMD. Further research is expected to explore the potential pathogenesis of MMD.

No abstract

No abstract

No abstract

No abstract