6 research outputs found

    Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems

    Get PDF
    One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure

    Evaluación del sistema de recomendación de patrones pedagógicos (SRPP) en cursos de Geometría Euclidiana

    Get PDF
    La situación del profesor universitario, a diferencia de otros colectivos docentes, se caracteriza por no tener una formación específica como profesionales de la enseñanza. Su formación, en cuanto a la docencia se deriva de su propia experiencia, y en muchos casos, los profesores universitarios carecen de instrumentos didácticos que les permitan analizar y reflexionar sobre su labor como docentes, y todo lo que ello supone (García-Valcárcel 2001). Las funciones del profesor universitario deben ser analizadas desde la concepción del mismo como un especialista de alto nivel dedicado a la enseñanza y miembro de una comunidad académica. Diremos que “el profesor universitario, en cuanto profesor, es una persona profesionalmente dedicada a la enseñanza, un profesional de la educación que necesariamente comparte con los profesores de otros niveles unas funciones básicas orientadas a que otras personas aprendan. En segundo lugar, es un especialista al más alto nivel en una ciencia, lo cual comporta la capacidad y hábitos investigadores que le permitan acercarse a, y ampliar, las fronteras de su rama del saber. En tercer lugar, es miembro de una comunidad académica, lo que supone la aceptación, y conformación de la conducta, a un conjunto específico de pautas, valores y actitudes que, de alguna manera, reflejan una determinada percepción de la realidad y caracterizan y dan sentido a una forma de vida” (De la Orden 1987). Es así como las funciones del profesor universitario son varias y con diferente carga de dedicación, interés y prestigio. Los ámbitos básicos de su dedicación son: la docencia, la investigación y la gestión, siendo esta última la actividad que en general es menos atractiva para ellos. Por su parte, la investigación es muy apreciada, y por ellos es la función que más tiempo consume y más beneficios reporta. En opinión de De Miguel (De Miguel 1991), el profesor universitario está demasiado imbuido en su rol de profesional o de científico de una disciplina, y desde ese rol intenta ejercer su acción docente. La consideración de “buen profesor” en el mundo universitario se ha ligada al concepto de “buen investigador”, generando con ello, algunos vacíos en los procesos de enseñanza aprendizaje

    Modeling medical devices for plug-and-play interoperability

    Get PDF
    Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 181-187).One of the challenges faced by clinical engineers is to support the connectivity and interoperability of medical-electrical point-of-care devices. A system that could enable plug-and-play connectivity and interoperability for medical devices would improve patient safety, save hospitals time and money, and provide data for electronic medical records. However, existing medical device connectivity standards, such as IEEE 11073, have not been widely adopted by medical device manufacturers. This lack of adoption is likely due to the complexity of the existing standards and their poor support for legacy devices. We attempted to design a simpler, more flexible standard for an integrated clinical environment manager. Our standard, called the ICEMAN standard, provides a meta-model for describing medical devices and a communication protocol to enable plug-and-play connectivity for compliant devices. To demonstrate the capabilities of ICEMAN standard, we implemented a service-oriented system that can pair application requirements with device capabilities, based on the ICEMAN device meta-model. This system enables medical devices to interoperate with the manager in a driverless fashion. The system was tested using simulated medical devices.by Robert Matthew Hofmann.M.Eng

    Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference

    Get PDF

    Advances in knowledge discovery and data mining Part II

    Get PDF
    19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p
    corecore