November 14, 2011 — Adolescent vaccination coverage is increasing but could be improved, according to a study published online November 14 in Pediatrics . Potential strategies include simultaneously administering all vaccines during checkups, improving clinician counseling and recommendation of vaccines, and increasing parental awareness and acceptance of recommendations from the Advisory Committee on Immunization Practices. During 2005 to 2007, the Advisory Committee on Immunization Practices expanded the recommended vaccination schedule for teenagers aged 13 to 17 years, adding a meningococcal conjugate vaccine (MenACWY), an acellular pertussis vaccine given in combination with the tetanus and diphtheria toxoids (TdaP), and for girls, 3 inoculations with the human papillomavirus vaccine (HPVV). An analysis of data from the provider-verified, random-digit-dial National Immunization Survey–Teen since its inception in 2006 reveals that vaccine administration is on the rise, but is not meeting full coverage potential. " urrent vaccination rates are still below target levels and are lower than levels achieved for vaccines routinely administered to children aged 19 to 35 months," the authors write, noting that Healthy People 2010 goals include achieving 80% coverage for 1 or more doses of TdaP vaccine, 1 or more doses of MenACWY vaccine, and 3 or more doses of HPVV. Between 2006 and 2009, 1 or more doses of TdaP vaccine and 1 or more doses of MenACWY vaccine coverage increased from 11% to 56% and 12% to 54%, respectively. Between 2007 and 2009, 1 or more doses of HPVV vaccine coverage among girls increased from 25% to 44%, with 3 or more doses of HPVV vaccine increasing from 18% to 27% between 2008 and 2009. The proportion of fully vaccinated teenagers increased from 10% in 2006 to 42% in 2009. Because infrequent healthcare visits are a major barrier when vaccinating teenagers, the authors used 2009 data to calculate potential coverage rates, had all recommended agents been administered simultaneously. They found that coverage could have been higher than 80% for the tetanus booster and meningitis shot, and as high as 74% for the first dose of HPVV. Total coverage could have been achieved for 76% of teenagers. The authors note that clinician counseling strongly influences parental acceptance of vaccines, and that providing a weak recommendation or delaying vaccination often signals that the inoculation is not needed or is not important. Top reported reasons for lack of TdaP and MenACWY vaccination included ignorance of the vaccine, lack of clinician recommendation, and a feeling that the vaccine was not necessary. With HPVV, belief in the teenager's lack of sexual activity proved to be an additional factor. Study limitations include the random-digit-dialed nature of the survey, which limits its scope to households with a landline, despite the high number of wireless-only households (25.9%), and potential underestimation of vaccination rates when health records could not be verified by a clinician. 来源： http://www.medscape.com/viewarticle/753511
Contents 1 周涛 个性化推荐的十大挑战 推荐引擎：信息暗海的领航员 Tag-Aware Recommender Systems: A State-of-the-Art Survey 1 周涛 个性化推荐的十大挑战 周涛 CCF通讯 第 8 卷 第 7 期 2012 年 7 月 关键词：个性化推荐 挑站 应用的算法和技术： 推荐项亮和陈义合著的《推荐系统实践》 挑战一：数据稀疏性问题 挑战二：冷启动问题 标签系统（tagging systems） 挑战三：大数据处理与增量计算问题 （latent dirichlet allocation, LDA）算法 挑战四：多样性与精确性的两难困境 挑战五：推荐系统的脆弱性问题 挑战六：用户行为模式的挖掘和利用 挑战七：推荐系统效果评估 图5总结了文献中曾经出现过的几乎所有的推荐系统指标 挑战八：用户界面与用户体验 挑战九：多维数据的交叉利用 网络与网络之间的相互作用 挑战十：社会推荐 个性化推荐的十大挑战.pdf 推荐引擎：信息暗海的领航员 Terry Lau 张韶峰 周 涛 CCF通讯 第 8 卷 第 6 期 2012 年 6 月 关键词：推荐引擎 电子商务 海量数据 1 引言 推荐引擎 - 信息过载 2 系统架构 如图1所示，百分点推荐引擎分为存储层、业务层、算法层和管理层四大功能组件 推荐引擎 信息暗海的领航员.pdf Tag-Aware Recommender Systems: A State-of-the-Art Survey Zi-Ke Zhang(张子柯), Tao Zhou (周涛), and Yi-Cheng Zhang JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 26(5): 767{777 Sept. 2011. Abstract In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms. Keywords social tagging systems, tag-aware recommendation, network-based/tensor-based/topic-based methods 1 Introduction an information overload: an urgent problem: how to automatically find out the relevant items for us? personalization = recommender system collaborative filtering (CF) obstacles confronted by CF: the sparsity of data reason: (i) the huge number of items are far beyond users' ability to evaluate even a small fraction of them; (ii) users do not incentively wish to rate the purchased/viewed items Q: User profiles vs. personal privacy. A: Attribute-aware method content-based algorithms vs. its limitation: the items contain rich content information that can be automatically extracted out network theory = complex networks. = folksonomy = social tags user-defined tags 2 Overview of Tag-Based Recommender Systems the influence of social tag on recommendation algorithms. -- FolkRank some open issues in tagging systems: (i) singularity vs. plurality: (ii) polysemy vs. synonymy: (iii) different online tagging systems allow users to give different formats of the tags Solutions: -- Firstly, clustering-based methods are proposed to alleviate the word reduction problem. -- Secondly, semantic methods are discussed to use ontology-based algorithms to organize the tags and reveal the semantic relations among them -- Thirdly, dimension reduction and topic-based methods are put forward to discover the latent topics, and graph-based methods are proposed to solve the sparsity problem in large-scale datasets. the orgnization of this paper: -- firstly give the evaluation metrics measured in this survey. -- Secondly we summarize some of the most recent and prominent tag-aware recommendation algorithms, showing and discussing how they make use of the aforementioned representations to address some unresolved issues in recommender systems. three kinds of recommendations in social tagging systems: (i) predicting friends to users; (ii) recommending items to users; (iii) pushing interesting topics (tags) to users. the most challenges in RS: filter irrelevant items for individuals the purpose of this paper: mainly discuss the second case, 3 Tag-Aware Recommendation Models a social tagging network consists of three different kinds of communities: users, items and tags, personomy: an entry set of personalized folksonomy, a full folksonomy can be considered in two ways to be: (i) three sets described by an adjacent matrix (ii) a ternary or hypergraph-based structure: 3.1 Evaluation Metrics each dataset,E: -- the training set -- the testing set 3.1.1 Metrics of Accuracy 1) Ranking Score (RS) 2) The Area under the ROC Curve 3) Recall recall: 3.1.2 Metrics of Diversity 1) Inter Diversity (InterD): measures the differences of different users' recommendation lists, 2) Inner Diversity (InnerD): measures the differences of items within a user's recommendation list 3.2 Network-Based Models mathematical modeling: tag-based network can be viewed as a tripartite graph which consists of three integrated bipartite graphs or a hypergraph. two underlying network-based methods: probability spreading (ProbS) and heat spreading (HeatS) ProbS: -- random walk (RW) in computer science -- mass diffusion (MD) in physics. HeatS: Table 1 shows the corresponding AUC results for three datasets: 3.3 Tensor-Based Models the tensor factorization (TF)-based method a ternary relation Fig.3 shows the illustration of the above two definitions. Y can be represented: The tensor factorization is based on singular value decomposition (SVD): 3.4 Topic-Based Models the core challenge of recommender systems: to estimate the likelihood between users and items. -- latent semantic analysis (LSA) -- the probability latent semantic analysis (PLSA) -- latent dirichlet allocation (LDA) 4 Conclusion and Outlook three aspects: (i) network-based methods; (ii) tensor-based methods; (iii) topic-based methods. 2011JCST-Tag_aware_recommender_systems.pdf
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