36,009 research outputs found

    Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

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    Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

    Using Intervention Mapping to Develop an Efficacious Multicomponent Systems-Based Intervention to Increase Human Papillomavirus (HPV) Vaccination in a Large Urban Pediatric Clinic Network

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    Background: The CDC recommends HPV vaccine for all adolescents to prevent cervical, anal, oropharyngeal, vaginal, vulvar, and penile cancers, and genital warts. HPV vaccine rates currently fall short of national vaccination goals. Despite evidence-based strategies with demonstrated efficacy to increase HPV vaccination rates, adoption and implementation of these strategies within clinics is lacking. The Adolescent Vaccination Program (AVP) is a multicomponent systems-based intervention designed to implement five evidence-based strategies within primary care pediatric practices. The AVP has demonstrated efficacy in increasing HPV vaccine initiation and completion among adolescents 10-17 years of age. The purpose of this paper is to describe the application of Intervention Mapping (IM) toward the development, implementation, and formative evaluation of the clinic-based AVP prototype. Methods: Intervention Mapping (IM) guided the development of the Adolescent Vaccination Program (AVP). Deliverables comprised: a logic model of the problem (IM Step 1); matrices of behavior change objectives (IM Step 2); a program planning document comprising scope, sequence, theory-based methods, and practical strategies (IM Step 3); functional AVP component prototypes (IM Step 4); and plans for implementation (IM Step 5) and evaluation (IM Step 6). Results: The AVP consists of six evidence-based strategies implemented in a successful sequenced roll-out that (1) established immunization champions in each clinic, (2) disseminated provider assessment and feedback reports with data-informed vaccination goals, (3) provided continued medical and nursing education (with ethics credit) on HPV, HPV vaccination, message bundling, and responding to parent hesitancy, (4) electronic health record cues to providers on patient eligibility, and (5) patient reminders for HPV vaccine initiation and completion. Conclusions: IM provided a logical and systematic approach to developing and evaluating a multicomponent systems-based intervention to increase HPV vaccination rates among adolescents in pediatric clinics

    An event distribution platform for recommending cultural activities

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    A personalized and context-aware news offer for mobile devices

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    For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer

    Exploration of the methodological quality and clinical usefulness of a cross-sectional sample of published guidance about exercise training and physical activity for the secondary prevention of coronary heart disease

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    Background Clinicians are encouraged to use guidelines to assist in providing evidence-based secondary prevention to patients with coronary heart disease. However, the expanding number of publications providing guidance about exercise training may confuse cardiac rehabilitation clinicians. We therefore sought to explore the number, scope, publication characteristics, methodological quality, and clinical usefulness of published exercise-based cardiac rehabilitation guidance. Methods We included publications recommending physical activity, exercise or cardiac rehabilitation for patients with coronary heart disease. These included systematically developed clinical practice guidelines, as well as other publications intended to support clinician decision making, such as position papers or consensus statements. Publications were obtained via electronic searches of preventive cardiology societies, guideline databases and PubMed, to November 2016. Publication characteristics were extracted, and two independent assessors evaluated quality using the 23-item Appraisal of Guidelines Research and Evaluation II (AGREE) tool. Results Fifty-four international publications from 1994 to 2016 were identified. Most were found on preventive cardiology association websites (n = 35; 65%) and were freely accessible (n = 50; 93%). Thirty (56%) publications contained only broad recommendations for physical activity and cardiac rehabilitation referral, while 24 (44%) contained the necessary detailed exercise training recommendations. Many were labelled as “guidelines”, however publications with other titles (e.g. scientific statements) were common (n = 24; 44%). This latter group of publications contained a significantly greater proportion of detailed exercise training recommendations than clinical guidelines (p = 0.017). Wide variation in quality also existed, with ‘applicability’ the worst scoring AGREE II domain for clinical guidelines (mean score 53%) and ‘rigour of development’ rated lowest for other guidance types (mean score 33%). Conclusions While a large number of guidance documents provide recommendations for exercise-based cardiac rehabilitation, most have limitations in either methodological quality or clinical usefulness. The lack of rigorously developed guidelines which also contain necessary detail about exercise training remains a substantial problem for clinicians

    Modeling User Viewing Flow using Large Language Models for Article Recommendation

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    This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles. Specifically, we employ a user constant viewing flow modeling method to summarize the user's general interest to recommend articles. We utilize Large Language Models (LLMs) to capture constant user preferences from previously clicked articles, such as skills and positions. Then we design the user instant viewing flow modeling method to build interactions between user-clicked article history and candidate articles. It attentively reads the representations of user-clicked articles and aims to learn the user's different interest views to match the candidate article. Our experimental results on the Alibaba Technology Association (ATA) website show the advantage of SINGLE, which achieves 2.4% improvements over previous baseline models in the online A/B test. Our further analyses illustrate that SINGLE has the ability to build a more tailored recommendation system by mimicking different article viewing behaviors of users and recommending more appropriate and diverse articles to match user interests.Comment: 8 pages
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