Enhancing the ELECTRE decision support method with semantic data

Prendre una decisió quan les opcions es defineixen mitjançant un conjunt divers de criteris no és fàcil. Aqueta tesi es centra en ampliar la metodologia ELECTRE, que és el mètode del tipus "outranking" més utilitzat. En aquesta tesi ens centrem en problemes de decisió que involucren informació no numèrica, tal com els criteris semàntics multivaluats, que poden prendre com a valors els conceptes d'una ontologia de domini determinada. Primer proposo una nova manera de manipular els criteris semàntics per evitar l'agregació de les puntuacions numèriques abans del procediment de classificació. Aquest mètode, anomenat ELECTRE-SEM, segueix els mateixos principis que el clàssic ELECTRE però, en aquest cas, els índexs de concordança i discordança es defineixen en termes de la comparació per parelles de les puntuacions que indiquen l'interès de l'usuari sobre diferents conceptes de l'ontologia. En segon lloc, proposo crear un perfil d'usuari semàntic mitjançant el emmagatzemant de puntuacions de preferències a l'ontologia. Es vincula una puntuació d'interès numèrica als conceptes més específics, això permet distingir millor les preferències de l'usuari, i també s'incorpora un procediment d'agregació per inferir les preferències de l'usuari considerant les relacions taxonòmiques entre conceptes. La metodologia proposada s'ha aplicat en dos casos d’estudi: l'avaluació de plantes de generació d'energia i la recomanació d'activitats turístiques a Tarragona.Tomar una decisión cuando las opciones se definen sobre un conjunto diverso de criterios no es fácil. Esta tesis se centra en ampliar la metodología ELECTRE, que es el método del tipo "outranking" más utilizado. En esta tesis nos centramos en problemas de decisión que involucren información no numérica, tal como los criterios semánticos multi-valuados, que pueden tomar como valores los conceptos de una ontología de dominio determinada. Primero propongo una nueva forma de manejar los criterios semánticos para evitar la agregación de puntuaciones numéricas antes del procedimiento de clasificación. Este método, llamado ELECTRE-SEM, sigue los mismos principios que el clásico ELECTRE, pero en este caso los índices de concordancia y discordancia se definen en términos de la comparación por pares de unas puntuaciones que indican el interés del usuario sobre distintos conceptos de la ontología. En segundo lugar, propongo crear un perfil de usuario semántico mediante el almacenamiento de puntuaciones de preferencias en la ontología. Se asocian puntuaciones numéricas a los conceptos más específicos, lo cual permite distinguir mejor las preferencias del usuario, y se incorpora un proceso de agregación para inferir las preferencias del usuario mediante las relaciones taxonómicas entre conceptos. La metodología propuesta ha sido aplicada en dos casos de estudio: la evaluación de las plantas de generación de energía y la recomendación de actividades turísticas en Tarragona.Reach a decision when options are defined on a set of diverse criteria is not easy. This thesis is focused on improving the methodology ELECTRE, which is the most used outranking-based method. In this dissertation, we focus on decision problems involving non-numerical information, such as multi-valued semantic criteria, which may take as values the concepts of a given domain ontology. First, I propose a new way of handling semantic criteria to avoid the aggregation of the numerical scores before the ranking procedure. This method, called ELECTRE-SEM, follows the same principles than the classic ELECTRE but in this case the concordance and discordance indices are defined in terms of the pairwise comparison of the interest scores. Second, I also propose to create a semantic user profile by storing preference scores into the ontology. The numerical interest score attached to the most specific concepts permits to distinguish better the preferences of the user, improving the quality of the decision by the incorporation of an aggregation methodology to infer the user's preferences by considering taxonomic relations between concepts. The proposed methodology has been applied in two case studies: the assessment of power generation plants and the recommendation of touristic activities in Tarragona

An Ontological Model of User Preferences

The notion of preferences plays an important role in many disciplines including service robotics which is concerned with scenarios in which robots interact with humans. These interactions can be favored by robots taking human preferences into account. This raises the issue of how preferences should be represented to support such preference-aware decision making. Several formal accounts for a notion of preferences exist. However, these approaches fall short on defining the nature and structure of the options that a robot has in a given situation. In this work, we thus investigate a formal model of preferences where options are non-atomic entities that are defined by the complex situations they bring about

An explainable recommender system based on semantically-aware matrix factorization.

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the ratings for unseen items with high accuracy. Matrix factorization is an accurate collaborative filtering method used to predict user preferences. However, it is a black box system that recommends items to users without being able to explain why. This is due to the type of information these systems use to build models. Although rich in information, user ratings do not adequately satisfy the need for explanation in certain domains. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of big data and machine learning methods to a mass audience without a compromise in trust. Explanations can take a variety of formats, depending on the recommendation domain and the machine learning model used to make predictions. Semantic Web (SW) technologies have been exploited increasingly in recommender systems in recent years. The SW consists of knowledge graphs (KGs) providing valuable information that can help improve the performance of recommender systems. Yet KGs, have not been used to explain recommendations in black box systems. In this dissertation, we exploit the power of the SW to build new explainable recommender systems. We use the SW\u27s rich expressive power of linked data, along with structured information search and understanding tools to explain predictions. More specifically, we take advantage of semantic data to learn a semantically aware latent space of users and items in the matrix factorization model-learning process to build richer, explainable recommendation models. Our off-line and on-line evaluation experiments show that our approach achieves accurate prediction with the additional ability to explain recommendations, in comparison to baseline approaches. By fostering explainability, we hope that our work contributes to more transparent, ethical machine learning without sacrificing accuracy

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc