Imagine that you get such certain awesome experience and knowledge by only reading a book. How can? It seems to be greater when a book can be the best thing to discover. Books now will appear in printed and soft file collection. One of them is this book personalization techniques and recommender systems vol 70. It is so usual with the printed books. However, many people sometimes have no space to bring the book for them; this is why they can't read the book wherever they want.

Introduction: The popularity of seeking health information online makes information quality (IQ) a public health issue. The present study aims at building a theoretical framework of health information quality (HIQ) that can be applied to websites and defines which IQ criteria are important for a website to be trustworthy and meet users' expectations.Methods: We have identified a list of HIQ criteria from existing tools and assessment criteria and elaborated them into a questionnaire that was promoted via social media and mainly the University. Responses (329) were used to rank the different criteria for their importance in trusting a website and to identify patterns of criteria using hierarchical cluster analysis.Results: HIQ criteria were organized in five dimensions based on previous theoretical frameworks as well as on how they cluster together in the questionnaire response. We could identify a top-ranking dimension (scientific completeness) that describes what the user is expecting to know from the websites (in particular: description of symptoms, treatments, side effects). Cluster analysis also identified a number of criteria borrowed from existing tools for assessing HIQ that could be subsumed to a broad “ethical” dimension (such as conflict of interests, privacy, advertising policies) that were, in general, ranked of low importance by the participants. Subgroup analysis revealed significant differences in the importance assigned to the various criteria based on gender, language and whether or not of biomedical educational background.Conclusions: We identified criteria of HIQ and organized them in dimensions. We observed that ethical criteria, while regarded highly in the academic and medical environment, are not considered highly by the public.

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Text classification is the problem of classifying a set of documents into a pre-defined set of classes. A major problem with text classification problems is the high dimensionality of the feature space. Only a small subset of these words are feature words which can be used in determining a document's class, while the rest adds noise and can make the results unreliable and significantly increase computational time. A common approach in dealing with this problem is feature selection where the number of words in the feature space are significantly reduced.In this paper we present the experiments of a comparative study of feature selection methods used for text classification. Ten feature selection methods were evaluated in this study including the new feature selection method, called the GU metric. The other feature selection methods evaluated in this study are: Chi-Squared (χ 2 ) statistic, NGL coefficient, GSS coefficient, Mutual Information, Information Gain, Odds Ratio, Term Frequency, Fisher Criterion, BSS/WSS coefficient. The experimental evaluations show that the GU metric obtained the best F 1 and F 2 scores. The experiments were performed on the 20 Newsgroups data sets with the Naive Bayesian Probabilistic Classifier.