Classification Rules to identify Context and Preference Information from Tourist’s Reviews

In many tourist sites have been incorporate box to allow people interchange experience, written comments and valuation about products or services. Many of the tourists planning decision are based on third-party opinions. Text mining is the discipline that extracts information from written text by users/consumers in natural language to be understood by a computer system. In this paper is presented a text mining process to obtain classification rules in order to identify context information and consumer’s preferences from a review. User’s preferences are different according with a situation or context in which the review was expressed. This approach was exemplified by a case study using reviews from www.tripadvisor.com.Sociedad Argentina de Informática e Investigación Operativ

Analítica del aprendizaje : método automático para identificar sentencias que contienen información positiva y negativa utilizando técnicas de minería de texto

Debido al avance y fácil acceso a la tecnología hoy en día, el uso de los sistemas de enseñanza y aprendizaje online se ha incrementado exponencialmente en los últimos años. Estos sistemas comúnmente llamados Entornos Virtuales de Aprendizaje, proporcionan herramientas para presentar contenido y recursos educativos, facilitar la comunicación e interacción entre los usuarios, herramientas de seguimiento y evaluación de la actividad de los estudiantes y en algunos casos herramientas de autor para crear contenido. Las interacciones de los usuarios con el sistema generan mucha información de gran valor que ayudan a los profesores a tomar decisiones. Los comentarios de los estudiantes en los foros o chats de las plataformas virtuales de aprendizajes son fuentes de información muy valiosas para aplicar analítica del aprendizaje. La información más relevante para obtener ciertas estadísticas de los estudiantes y su contexto está en los comentarios que ellos expresan de forma libre utilizando las herramientas de interacción. En este artículo se presenta una técnica para identificar sentencias que contienen información positiva y negativa relevante e informar al profesor acerca de los aspectos negativos que puedan dar origen a posibles abandonos o problemas en el aprendizaje

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Negotiation-Style Recommender Based on Computational Ecology in Open Negotiation Environments.

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies.Fil: De La Rosa, Josep Lluis. Universidad de Girona; EspañaFil: Hormazábal, Nicolás. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Lopardo, Gabriel Alejandro. Universidad de Girona; EspañaFil: Trias, Albert. Universidad de Girona; EspañaFil: Montaner, Miquel. No especifíca

Recommender Systems in Serious Games

El objetivo de este trabajo ha sido conseguir un sistema inteligente que analice las acciones del usuario con el sistema y en función de su interacción y evolución recomiende las actividades a realizar con el mismo. Las principales aportaciones de este trabajo consisten en la creación de un sistema de recomendación de actividades basado en diferentes niveles de complejidad y un sistema de habilidades del usuario. El sistema recomendador por tanto, tiene la funcionalidad de proporcionar al usuario un modo de juego personalizado en base a la interacción que tenga con el sistema y la evolución de sus resultados. Este sistema de recomendación se ha aplicado a la plataforma Tango:H. En particular, se han modificado los dos módulos principales de esta plataforma, compuesto por: Tango:H y TangoH: Designer, además el módulo Tango:H SQilite, a través del cual, se controla el sistema de base de datos.The objective of this work has been to achieve an intelligent system that analyzes the user's actions with the system and, based on their interaction and evolution, recommend the activities to be carried out with it. The main contributions of this work consist of the creation of a recommendation system of activities based on different levels of complexity and a system of user skills. The recommender system therefore has the functionality of providing the user with a personalized game mode based on the interaction with the system and the evolution of its results. This recommendation system has been applied to the Tango platform: H. In particular, the two main modules of this platform have been modified, consisting of: Tango: H and TangoH: Designer, in addition to the Tango module: H SQilite, through which the database system is controlled

Information sources selection methodology for recommender systems based on intrinsic characteristics and trust measure

El treball desenvolupat en aquesta tesi presenta un profund estudi i proveïx solucions innovadores en el camp dels sistemes recomanadors. Els mètodes que usen aquests sistemes per a realitzar les recomanacions, mètodes com el Filtrat Basat en Continguts (FBC), el Filtrat Col·laboratiu (FC) i el Filtrat Basat en Coneixement (FBC), requereixen informació dels usuaris per a predir les preferències per certs productes. Aquesta informació pot ser demogràfica (Gènere, edat, adreça, etc), o avaluacions donades sobre algun producte que van comprar en el passat o informació sobre els seus interessos. Existeixen dues formes d'obtenir aquesta informació: els usuaris ofereixen explícitament aquesta informació o el sistema pot adquirir la informació implícita disponible en les transaccions o historial de recerca dels usuaris. Per exemple, el sistema recomanador de pel·lícules MovieLens (http://movielens.umn.edu/login) demana als usuaris que avaluïn almenys 15 pel·lícules dintre d'una escala de * a * * * * * (horrible, ...., ha de ser vista). El sistema genera recomanacions sobre la base d'aquestes avaluacions. Quan els usuaris no estan registrat en el sistema i aquest no té informació d'ells, alguns sistemes realitzen les recomanacions tenint en compte l'historial de navegació. Amazon.com (http://www.amazon.com) realitza les recomanacions tenint en compte les recerques que un usuari a fet o recomana el producte més venut. No obstant això, aquests sistemes pateixen de certa falta d'informació. Aquest problema és generalment resolt amb l'adquisició d'informació addicional, se li pregunta als usuaris sobre els seus interessos o es cerca aquesta informació en fonts addicionals. La solució proposada en aquesta tesi és buscar aquesta informació en diverses fonts, específicament aquelles que contenen informació implícita sobre les preferències dels usuaris. Aquestes fonts poden ser estructurades com les bases de dades amb informació de compres o poden ser no estructurades com les pàgines web on els usuaris deixen la seva opinió sobre algun producte que van comprar o posseïxen.Nosaltres trobem tres problemes fonamentals per a aconseguir aquest objectiu: 1 . La identificació de fonts amb informació idònia per als sistemes recomanadors.2 . La definició de criteris que permetin la comparança i selecció de les fonts més idònies. 3 . La recuperació d'informació de fonts no estructurades. En aquest sentit, en la tesi proposada s'ha desenvolupat: 1 . Una metodologia que permet la identificació i selecció de les fonts més idònies. Criteris basats en les característiques de les fonts i una mesura de confiança han estat utilitzats per a resoldre el problema de la identificació i selecció de les fonts. 2 . Un mecanisme per a recuperar la informació no estructurada dels usuaris disponible en la web. Tècniques de Text Mining i ontologies s'han utilitzat per a extreure informació i estructurar-la apropiadament perquè la utilitzin els recomanadors. Les contribucions del treball desenvolupat en aquesta tesi doctoral són: 1. Definició d'un conjunt de característiques per a classificar fonts rellevants per als sistemes recomanadors2. Desenvolupament d'una mesura de rellevància de les fonts calculada sobre la base de les característiques definides3. Aplicació d'una mesura de confiança per a obtenir les fonts més fiables. La confiança es definida des de la perspectiva de millora de la recomanació, una font fiable és aquella que permet millorar les recomanacions. 4. Desenvolupament d'un algorisme per a seleccionar, des d'un conjunt de fonts possibles, les més rellevants i fiable utilitzant les mitjanes esmentades en els punts previs. 5. Definició d'una ontologia per a estructurar la informació sobre les preferències dels usuaris que estan disponibles en Internet. 6. Creació d'un procés de mapatge que extreu automàticament informació de les preferències dels usuaris disponibles en la web i posa aquesta informació dintre de l'ontologia. Aquestes contribucions permeten aconseguir dos objectius importants: 1 . Millorament de les recomanacions usant fonts d'informació alternatives que sigui rellevants i fiables.2 . Obtenir informació implícita dels usuaris disponible en Internet.The work developed in this thesis presents an in-depth study and provides innovative solutions in the field of recommender systems. The methods used by these systems to carry out recommendations, such as Content-Based Filtering (CBF), Collaborative Filtering (CF) and Knowledge-Based Filtering (KBF), require information from users to predict preferences for certain products. This may be demographic information (genre, age and address), evaluations given to certain products in the past or information about their interests. There are two ways of obtaining this information: users offer it explicitly or the system can retrieve the implicit information available in the purchase and search history. For example, the movie recommender system MovieLens (http://movielens.umn.edu/login) asks users to rate at least 15 movies on a scale of * to * * * * * (awful, ... , must be seen). The system generates recommendations based on these evaluations. When users are not registered into the site and it has no information about them, recommender systems make recommendations according to the site search history. Amazon.com (http://www.amazon.com) make recommendations according to the site search history or recommend the best selling products. Nevertheless, these systems suffer from a certain lack of information. This problem is generally solved with the acquisition of additional information; users are asked about their interests or that information is searched for in additional available sources. The solution proposed in this thesis is to look for that information in various sources, specifically those that contain implicit information about user preferences. These sources can be structured like databases with purchasing information or they can be unstructured sources like review pages where users write their experiences and opinions about a product they buy or possess.We have found three fundamental problems to achieve this objective: 1. The identification of sources with suitable information for recommender systems.2. The definition of criteria that allows the comparison and selection of the most suitable sources.3. Retrieving the information from unstructured sources.In this sense, the proposed thesis has developed:1. A methodology that allows the identification and selection of the most suitable sources. Criteria based on the characteristics of sources and a trust measure have been used to solve the problem of identifying and selecting sources.2. A mechanism to retrieve unstructured information from users available on the Web. Text mining techniques and ontologies have been used to extract information and structure it appropriately for use by the recommenders.The contributions of the work developed in this doctoral thesis are:1. Definition of a set of characteristics to classify relevant sources of information for recommender systems.2. Development of a measure of relevance of sources according to characteristics defined in previous point.3. Application of a trust measure to obtain the most reliable sources. Confidence is measured from the perspective of improving the recommendation; a reliable source is one that leads to improved recommendations.4. Development of an algorithm to select, from a set of possible sources, the most relevant and reliable ones according to measures defined in previous points.5. Definition of an ontology to structure information about user preferences that are available on the Internet.6. The creation of a mapping process that automatically extracts information about user preferences available on the web and put in the ontology.These contributions allow us the achievement of two important objectives:1. Improving recommendations using alternative sources of information that are relevant and trustworthy.2. Obtaining implicit information about user available on the Internet

Classifying user experience based on the intention to communicate

Experience mining is considered a substantial extension of opinion mining. Experience mining covers the description of all events that are related to the user's perception in the interaction with the object. There is information about the user's experience that cannot be obtained with polarity analysis or sentiment analysis. The information obtained from those analyses is the positive or negative evaluation of a product/service. But in opinion analysis there is more useful information for other users. In this work we propose a method to obtain expressions of user experiences that are difficult to obtain by some of the current methods. The contributions of this work are: 1) Classification of information of user experiences according to the intention of communication: the categories are: "Evaluation", "Sensation" "Recommendation" and Target Users". 2) A rule-based method: rules take into account the lexical classification of words to classify sentences based on communicative intention. The results obtained show that the method based on classification rules can provide useful information about the user experience to other users.Fil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Cs.exactas Físicas y Naturales. Instituto de Informatica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Ochs, Magalie. Aix-Marseille Université; Franci

Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users

Enhancing Recommender System with Collaborative Filtering and User Experiences Filtering

Recommender systems have become an essential part in many applications and websites to address the information overload problem. For example, people read opinions about recommended products before buying them. This action is time-consuming due to the number of opinions available. It is necessary to provide recommender systems with methods that add information about the experiences of other users, along with the presentation of the recommended products. These methods should help users by filtering reviews and presenting the necessary answers to their questions about recommended products. The contribution of this work is the description of a recommender system that recommends products using a collaborative filtering method, and which adds only relevant feedback from other users about recommended products. A prototype of a hotel recommender system was implemented and validated with real users

Analítica del aprendizaje: método automático para identificar sentencias que contienen información positiva y negativa utilizando técnicas de minería de texto

Debido al avance y fácil acceso a la tecnología hoy en día, el uso de los sistemas de enseñanza y aprendizaje online se ha incrementado exponencialmente en los últimos años. Estos sistemas comúnmente llamados Entornos Virtuales de Aprendizaje, proporcionan herramientas para presentar contenido y recursos educativos, facilitar la comunicación e interacción entre los usuarios, herramientas de seguimiento y evaluación de la actividad de los estudiantes y en algunos casos herramientas de autor para crear contenido. Las interacciones de los usuarios con el sistema generan mucha información de gran valor que ayudan a los profesores a tomar decisiones. Los comentarios de los estudiantes en los foros o chats de las plataformas virtuales de aprendizajes son fuentes de información muy valiosas para aplicar analítica del aprendizaje. La información más relevante para obtener ciertas estadísticas de los estudiantes y su contexto está en los comentarios que ellos expresan de forma libre utilizando las herramientas de interacción. En este artículo se presenta una técnica para identificar sentencias que contienen información positiva y negativa relevante e informar al profesor acerca de los aspectos negativos que puedan dar origen a posibles abandonos o problemas en el aprendizaje.Fil: Aciar, Silvana Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Cs.exactas Físicas y Naturales. Instituto de Informatica; ArgentinaFil: González González, Carina Soledad. Universidad de La Laguna; EspañaFil: Aciar, Gabriela Iris. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaVIII Jornadas Internacionales de Campus VirtualesTenerifeEspañaUniversidad de de La Lagun