Social software for music

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Recommender systems in industrial contexts

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201

Modelo inteligente de recomendación de campañas, basado en perfilamiento de hábitos de consumo PI14104

Sistemas de segmentación y envío de campañas masivas, no se basan en un análisis exhaustivo a nivel individual de preferencias e intereses expresados e inferidos, que son establecidos mediante el análisis de varias fuentes de datos. Permitiendo mejorar no solo el conocimiento acerca del cliente, si no a su vez reaccionar a nuevos intereses, mejorando el nivel de efectividad en la recomendación. Para esto, se plantea la creación de un modelo inteligente de recomendación de campañas, basado en perfilamiento de hábitos de consumo. De manera que, reaccione proveyendo oferta adaptada a preferencias y necesidades del individuo, por medio del análisis y procesamiento de eventos del mundo físico y virtual como: Eventos en redes sociales, eventos de ubicación geográfica y eventos provenientes de transacciones financieras.Segmentation and sent to mass system campaigns, aren t based on a thorough analysis of individual preferences and interests expressed and inferred. Which can be performed throw data analyzing on different sources. Allowing to improve don t only knowledge about customer, also to turn react to new likes and improve effectiveness level on recommendation. Hence we arise creation of a profiling and consumption habits intelligent system based, which react providing personalized offers, against individual preferences and needs, through events processing on physical and real world, like events on networks, geographical location and financial transaction.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí