Today recommenders are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/searched/visited item and all the items stored in a repository. It is a common belief that the user is interested in what issimilar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. This paper presents the design and implementation of a hybrid recommender system that joins a contentbased approach and serendipitous heuristics in order to mitigate the over-specialization problem with surprising suggestions. Background and MotivationInformation overload is a common issue among the modern information society. Information Filtering (IF) is a kind of intelligent computing techniques that mitigates this problem by providing the user with the most relevant information with respect to her information needs.Recommender systems (RSs) adopt IF techniques in order to provide customized information access for targeted domains.They can be viewed as intelligent systems that take input directly or indirectly from users and, based on their needs, preferences and usage patterns, provide personalized advices about products or services and can help people to filter useful information.Several definitions of RS have been given. According to [3]: "Recommender systems have the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options". This definition makes it clear that user oriented guidance is critical in a RS.Among different recommendation techniques proposed in the literature, the content-based and the collaborative filtering approaches are the most widely adopted to date. Systems implementing the contentbased recommendation approach analyze a set of documents, usually textual descriptions of the items previously rated by an individual user, and build a model or profile of user interests based on the features of the objects rated by that user [14]. The profile is exploited to recommend new items of interest. Collaborative recommenders differ from content-based ones in that user opinions are used instead of content. They gather ratings about objects by users and store them in a centralized or distributed database. To provide user X with recommendations, the system computes the neighborhood of that user, i.e. the subset of users that have a taste similar to X. Similarity in taste is computed based on the similarity of ratings for objects that were rated by both users. The system then recommends objects that users in X's neighborhood indicated to like, provided that they have not yet been rated by X. Each type of filtering methods has its own weaknesses and strengths.In particular, the content-ba...

No abstract

No abstract

No abstract

A simple self-administered questionnaire was mailed to a population sample of 8,626 (40–65 years old) to identify transient ischemic attacks (TIAs) that occurred in the previous 12 months. This study was conducted in a well-defined, medically controlled geographic area. 75.4% of the questionnaires were returned. The procedure identified 52 TIA cases (43 definite and 9 uncertain). The 12-month period prevalence for TIAs was 6.6 per 1,000 (95% confidence limits of 4.8–8.9) among the respondents. The annual incidence rate for first TIAs was 3.1 per 1,000 (95% confidence limits of 1.9–4.7). Our results differ from those reported in hospital series or in population surveys based on clinical records, with higher incidence and prevalence rates, female preponderate and higher frequency of vertebrobasilar attacks.