Abstract. Collaborative filtering aims at helping users find items they should appreciate from huge catalogues. In that field, we can distinguish user-based, item-based and model-based approaches. For each of them, many options play a crucial role for their performances, and in particular the similarity function defined between users or items, the number of neighbors considered for user-or item-based approaches, the number of clusters for model-based approaches using clustering, and the prediction function used. In this paper, we review the main collaborative filtering methods proposed in the litterature and compare them on the same widely used real dataset called MovieLens, and using the same widely used performance measure called Mean Absolute Error (MAE). This study thus allows us to highlight the advantages and drawbacks of each approach, and to propose some default options that we think should be used when using a given approach or designing a new one.

Abstract. The aim of collaborative filtering is to help users to find items that they should appreciate from huge catalogues. In that field, we can distinguish user-based from item-based approaches. The former is based on the notion of user neighbourhoods while the latter uses item neighbourhoods. The definition of similarity between users and items is a key problem in both approaches. While traditional similarity measures can be used, we will see in this paper that bespoke ones, that are tailored to type of data that is typically available (i.e. very sparse), tend to lead to better results. Extensive experiments are conducted on two publicly available datasets, called MovieLens and Netflix. Many similarity measures are compared. And we will show that using weighted similarity measures significantly improves the results of both user-and item-based approaches.

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IntroductionWomen with gestational diabetes mellitus (GDM) have a higher risk of developing type 2 diabetes mellitus compared with women who never had GDM. Consequently, the question of structured aftercare for GDM has emerged. In all probability, many women do not receive care according to the guidelines. In particular, the process and interaction between obstetrical, diabetic, gynaecological, paediatric and general practitioner care lacks clear definitions. Thus, our first goal is to analyse the current aftercare situation for women with GDM in Germany, for example, the participation rate in aftercare diabetes screening, as well as reasons and attitudes stated by healthcare providers to offer these services and by patients to participate (or not). Second, we want to develop an appropriate, effective and patient-centred care model.Methods and analysisThis is a population-based mixed methods study using both quantitative and qualitative research approaches. In various working packages, we evaluate data of the GestDiab register, of the Association of Statutory Health Insurance Physicians of North Rhine and the participating insurance companies (AOK Rheinland/Hamburg, BARMER, DAK Gesundheit, IKK classic, pronova BKK). In addition, quantitative (postal surveys) and qualitative (interviews) surveys will be conducted with randomly selected healthcare providers (diabetologists, gynaecologists, paediatricians and midwives) and affected women, to be subsequently analysed. All results will then be jointly examined and evaluated.Ethics and disseminationThe study was approved by the ethics committee of the Faculty of Medicine, Heinrich-Heine-University Düsseldorf (Ethics Committee No.: 2019-738). Participants of the postal surveys and interviews will be informed in detail about the study and the use of data as well as the underlying data protection regulations before voluntarily participating. The study results will be disseminated through peer-reviewed journals, conferences and public information.Trial registration numberDRKS00020283.

In a representative 2-year period (1997-1998), drug monitoring was performed for 6293 patients, and 569 drug-related inquiries were answered. The main categories of required drug information were: pharmacokinetics/metabolism/analytical problems (38%), adverse drug reactions/drug safety (24%), therapeutic drug use/drug indication (21%) and pharmaceuticals (9%). Further activities of the institute refer to teaching of undergraduates, continuing education of local physicians in recent aspects of drug therapy, providing therapeutic bulletins and conducting supporting clinical studies.

The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative filtering approaches can be distinguished from content-based ones. The former is based on a set of user ratings on items, while the latter uses item content descriptions and user thematic profiles. While collaborative filtering systems often result in better predictive performance, content-based filtering offers solutions to the limitations of collaborative filtering, as well as a natural way to interact with users. These complementary approaches thus motivate the design of hybrid systems. In this chapter, the main algorithmic methods used for recommender systems are presented in a state of the art. The evaluation of recommender systems is currently an important issue. The authors focus on two kinds of evaluations. The first one concerns the performance accuracy: several approaches are compared through experiments on two real movies rating datasets MovieLens and Netflix. The second concerns user satisfaction and for this a hybrid system is implemented and tested with real users.