Recommender systems evaluate and filter the vast amount of information available on theWeb, so th... more Recommender systems evaluate and filter the vast amount of information available on theWeb, so they can be used to assist users in the process of accessing to relevant information. In the literature we can find countless approaches for generating personalized recommendations and all of them make use of dierent users’ and/or items’ features. In this sense, building accurate profiles plays an essential role in this context making the system’s success depend to a large extent on the ability of the learned profiles to represent the user’s preferences and needs. An ontology works very well to characterize the users profiles involved in the process of generating recommendations. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modeling, so that in the recommendation generation process we don’t take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback.
ABSTRACT Social Media can be used as a thermometer to measure how society perceives different new... more ABSTRACT Social Media can be used as a thermometer to measure how society perceives different news and topics. With the advent of mobile devices, users can interact with Social Media platforms anytime/anywhere, increasing the proportion of geo-located Social Media interactions and opening new doors to localized insights. This article suggests a new method built upon the industry standard Recency, Frequency and Monetary model to quantify the impact of a topic on a defined geographical location during a given period of time. We model each component with a set of metrics analyzing how users in the location actively engage with the topic and how they are exposed to the interactions in their Social Media network related to the topic. Our method implements a full fledged information extraction system consuming geo-localized Social Media interactions and generating on a regular basis the impact quantification metrics. To validate our approach, we analyze its performance in two real-world cases using geo-located tweets.
In certain domains with a dynamic research activity, such as that of Biomedical Sciences, it is n... more In certain domains with a dynamic research activity, such as that of Biomedical Sciences, it is necessary the development of new services capable of satisfying their specific information needs. In this paper we present a filtering and recommender system that applies Semantic Web technologies and Fuzzy Linguistic Modeling techniques in order to provide users valuable information about resources that fit
European Society for Fuzzy Logic and Technology, 2005
In this paper a new modelling for a weighted Information Retrieval System (IRS) in a linguistic c... more In this paper a new modelling for a weighted Information Retrieval System (IRS) in a linguistic context is proposed. This linguistic IRS (LIRS) achieves more precise and consistent relevance degrees that early weighted IRSs proposed (8, 9). To do this, a new redefinition of matching function defined in (11) is used.
Resumen Los sistemas de recomendaciones son herramientas que generan recomendaciones sobre un det... more Resumen Los sistemas de recomendaciones son herramientas que generan recomendaciones sobre un determinado objeto de estudio, a partir de las p referencias y opiniones dadas por los usuarios. El uso de estos sistemas se está poniendo cada vez más de moda en Internet debido a que son muy útiles para evaluar y filtrar la gran cantidad de información disponible en
Astrophysics and Space Science - ASTROPHYS SPACE SCI, 1997
We propose new parameters to describe the geometry of a warped disc when viewed edge-on, a global... more We propose new parameters to describe the geometry of a warped disc when viewed edge-on, a global warp parameter w, and a family of three parameters A, B and C that describe, independently, the shape of the warp. These parameters are useful to detect some key effects in the understanding of warps. We have also developed software (WIG) which is able to calculate these parameters for a given image of a galaxy in any wavelength and which is described here with some examples, namely ESO 235–53 in the optical, NGC 4013 in 21 cm and NGC 4565 in 1.2 mm continuum.
Resumen—En este trabajo se presenta el diseño de un sistema de recomendaciones lingüístico difuso... more Resumen—En este trabajo se presenta el diseño de un sistema de recomendaciones lingüístico difuso para facilitar a los alumnos el acceso a información sobre recursos docentes que puedan ser de su interés. Al sugerir material didáctico adecuado a las necesidades específicas del alumno, se fomenta un aprendizaje significativo, incidiendo directamente en el proceso de enseñanza- aprendizaje. El uso de dicho
The performance of Information Retrieval Systems (IRSs) is usually measured using two different c... more The performance of Information Retrieval Systems (IRSs) is usually measured using two different criteria, precision and recall. In such a way, the problem of tuning an IRS may be considered as a multi-objective optimization problem. In this contribution, we focus on the automatic learning of Boolean queries in IRSs by means of multi-objective evolutionary techniques. We present a comparative study of four multi-objective evolutionary optimization techniques of general-purpose (NSGA-II, SPEA2 and two MOGLS) to learn Boolean queries.
Recommender systems evaluate and filter the vast amount of information available on theWeb, so th... more Recommender systems evaluate and filter the vast amount of information available on theWeb, so they can be used to assist users in the process of accessing to relevant information. In the literature we can find countless approaches for generating personalized recommendations and all of them make use of dierent users’ and/or items’ features. In this sense, building accurate profiles plays an essential role in this context making the system’s success depend to a large extent on the ability of the learned profiles to represent the user’s preferences and needs. An ontology works very well to characterize the users profiles involved in the process of generating recommendations. In this paper we develop an ontology to characterize the trust between users using the fuzzy linguistic modeling, so that in the recommendation generation process we don’t take into account users with similar ratings history but users in which each user can trust. We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and to update the user profiles based on the feedback.
ABSTRACT Social Media can be used as a thermometer to measure how society perceives different new... more ABSTRACT Social Media can be used as a thermometer to measure how society perceives different news and topics. With the advent of mobile devices, users can interact with Social Media platforms anytime/anywhere, increasing the proportion of geo-located Social Media interactions and opening new doors to localized insights. This article suggests a new method built upon the industry standard Recency, Frequency and Monetary model to quantify the impact of a topic on a defined geographical location during a given period of time. We model each component with a set of metrics analyzing how users in the location actively engage with the topic and how they are exposed to the interactions in their Social Media network related to the topic. Our method implements a full fledged information extraction system consuming geo-localized Social Media interactions and generating on a regular basis the impact quantification metrics. To validate our approach, we analyze its performance in two real-world cases using geo-located tweets.
In certain domains with a dynamic research activity, such as that of Biomedical Sciences, it is n... more In certain domains with a dynamic research activity, such as that of Biomedical Sciences, it is necessary the development of new services capable of satisfying their specific information needs. In this paper we present a filtering and recommender system that applies Semantic Web technologies and Fuzzy Linguistic Modeling techniques in order to provide users valuable information about resources that fit
European Society for Fuzzy Logic and Technology, 2005
In this paper a new modelling for a weighted Information Retrieval System (IRS) in a linguistic c... more In this paper a new modelling for a weighted Information Retrieval System (IRS) in a linguistic context is proposed. This linguistic IRS (LIRS) achieves more precise and consistent relevance degrees that early weighted IRSs proposed (8, 9). To do this, a new redefinition of matching function defined in (11) is used.
Resumen Los sistemas de recomendaciones son herramientas que generan recomendaciones sobre un det... more Resumen Los sistemas de recomendaciones son herramientas que generan recomendaciones sobre un determinado objeto de estudio, a partir de las p referencias y opiniones dadas por los usuarios. El uso de estos sistemas se está poniendo cada vez más de moda en Internet debido a que son muy útiles para evaluar y filtrar la gran cantidad de información disponible en
Astrophysics and Space Science - ASTROPHYS SPACE SCI, 1997
We propose new parameters to describe the geometry of a warped disc when viewed edge-on, a global... more We propose new parameters to describe the geometry of a warped disc when viewed edge-on, a global warp parameter w, and a family of three parameters A, B and C that describe, independently, the shape of the warp. These parameters are useful to detect some key effects in the understanding of warps. We have also developed software (WIG) which is able to calculate these parameters for a given image of a galaxy in any wavelength and which is described here with some examples, namely ESO 235–53 in the optical, NGC 4013 in 21 cm and NGC 4565 in 1.2 mm continuum.
Resumen—En este trabajo se presenta el diseño de un sistema de recomendaciones lingüístico difuso... more Resumen—En este trabajo se presenta el diseño de un sistema de recomendaciones lingüístico difuso para facilitar a los alumnos el acceso a información sobre recursos docentes que puedan ser de su interés. Al sugerir material didáctico adecuado a las necesidades específicas del alumno, se fomenta un aprendizaje significativo, incidiendo directamente en el proceso de enseñanza- aprendizaje. El uso de dicho
The performance of Information Retrieval Systems (IRSs) is usually measured using two different c... more The performance of Information Retrieval Systems (IRSs) is usually measured using two different criteria, precision and recall. In such a way, the problem of tuning an IRS may be considered as a multi-objective optimization problem. In this contribution, we focus on the automatic learning of Boolean queries in IRSs by means of multi-objective evolutionary techniques. We present a comparative study of four multi-objective evolutionary optimization techniques of general-purpose (NSGA-II, SPEA2 and two MOGLS) to learn Boolean queries.
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Papers by Carlos Porcel
assist users in the process of accessing to relevant information. In the literature we can find countless approaches for
generating personalized recommendations and all of them make use of dierent users’ and/or items’ features. In this
sense, building accurate profiles plays an essential role in this context making the system’s success depend to a large
extent on the ability of the learned profiles to represent the user’s preferences and needs. An ontology works very well
to characterize the users profiles involved in the process of generating recommendations. In this paper we develop an
ontology to characterize the trust between users using the fuzzy linguistic modeling, so that in the recommendation
generation process we don’t take into account users with similar ratings history but users in which each user can trust.
We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and
to update the user profiles based on the feedback.
assist users in the process of accessing to relevant information. In the literature we can find countless approaches for
generating personalized recommendations and all of them make use of dierent users’ and/or items’ features. In this
sense, building accurate profiles plays an essential role in this context making the system’s success depend to a large
extent on the ability of the learned profiles to represent the user’s preferences and needs. An ontology works very well
to characterize the users profiles involved in the process of generating recommendations. In this paper we develop an
ontology to characterize the trust between users using the fuzzy linguistic modeling, so that in the recommendation
generation process we don’t take into account users with similar ratings history but users in which each user can trust.
We present our ontology and provide a method to aggregate the trust information captured in the trust-ontology and
to update the user profiles based on the feedback.