Filtragem colaborativa aplicada à recomendaçao musical

English: Some years ago, some songs were really rarities almost impossible to find, and the access to non-commercial music had very large demographic constraints. People used to have a limited amount of music available, and it was not too difficult for a person to know the songs he liked from the ones he could hear. Currently, with the development of new technologies in recent decades, it is possible to obtain a variety of new songs in minutes, without needing to leave home. In front of this range of possibilities, it becomes a daunting task for users to search the world?s music?s that best suit with their taste. But new technologies also help users in this quest. This paper presents a study of different methods based on collaborative filtering to provide personalized recommendations to users. The main information used in the strategy is the frequency of the users listened songs and the ones listened by similar users. The experiments focus on the use of the kNN algorithm to predict the frequency with which a particular user will hear a particular song. The results of the kNN prediction are compared with the results of basic predictors? prediction, based on means.Castellano: No hace tantos años, algunas músicas eran realmente rarezas casi imposibles de encontrar, y el acceso a músicas no comerciales tenia limitaciones demográficas muy grandes. Las personas tenían una cantidad limitada de músicas a su disposición, y no era demasiado difícil para una persona saber las canciones que le gustaban de entre las que podía escuchar. Actualmente, con el desarrollo de las nuevas tecnologías en las últimas décadas, es posible obtener una gran diversidad de nuevas canciones en pocos minutos, sin precisar salir de casa. Delante de este abanico de posibilidades, se convierte en una ardua tarea para los usuarios buscar las músicas del mundo que mejor de adapten a su gusto. Pero las nuevas tecnologías también permiten ayudar a los usuarios en esta búsqueda. Este trabajo presenta un estudio de diferentes métodos basados en filtraje colaborativo para proporcionar recomendaciones personalizadas a los usuarios. La principal información utilizada en la estrategia es la frecuencia de las canciones ya escuchadas por los usuarios y por otros usuarios semejantes. Los experimentos realizados focalizan en el uso del algoritmo kNN para predecir la frecuencia con la cual un determinado usuario escuchará una canción en particular. Los resultados de la predicción con el kNN son comparados con los resultados de la predicción con predictores básicos, basados en las medias.Català: No fa gaires anys, algunes músiques eren realment rareses quasi impossibles de trobar, i l'accés a músiques no comercials tenia limitacions demogràfiques molt grans. Les persones tenien una quantitat limitada de músiques a la seva disposició, i no era gaire difícil per una persona saber les cançons que li agradaven d'entre les que podia escoltar. Actualment, amb el desenvolupament de les noves tecnologies en les darreres dècades, és possible obtenir una gran diversitat de cançons noves en pocs minuts, sense necessitat de sortir de casa. Davant d'aquest ventall de possibilitats, esdevé una feina feixuga per als usuaris cercar les músiques del món que millor s'adapten als seus gustos. Però les noves tecnologies també permeten ajudar els usuaris en aquesta recerca. Aquest treball presenta un estudi de diferents mètodes basats en el filtrat col.laboratiu per proporcionar recomanacions personalitzades als usuaris. La principal informació utilitzada en l'estratègia és la freqüència de les cançons ja escoltades pels usuaris i per altres usuaris semblants. Els experiments realitzats focalitzen en l'ús de l'algoritme kNN per predir la freqüència amb la qual un determinat usuari escoltarà una cançó en particular. Els resultats de la predicció amb el kNN són comparats amb els resultats de la predicció amb predictors bàsics, basats en les mitjanes

Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities

Lifelong learners who assign learning activities (from multiple sources) to attain certain learning goals throughout their lives need to know which learning activities are (most) suitable and in which sequence these should be performed. Learners need support in this way finding process (selection and sequencing), and we argue this could be provided by using personalised recommender systems. To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way finding has been developed that presents personalised recommendations in relation to information about learning goals, learning activities and learners. A personalised recommender system has been developed accordingly, and recommends learners on the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed

Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities

Hummel, H. G. K., Van den Berg, E. J., Berlanga, A. J., Drachsler, H., Janssen, J., Nadolski, R.J., & Koper, E.J.R. (2007). Combining social- and information-based approaches for personalised recommendation on sequencing learning activities. International Journal of Learning Technology, 3(2), 152-168.Lifelong learners who assign learning activities (from multiple sources) to attain certain learning goals throughout their lives need to know which learning activities are (most) suitable and in which sequence these should be performed. Learners need support in this way finding process (selection and sequencing), and we argue this could be provided by using personalised recommender systems. To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way finding has been developed that presents personalised recommendations in relation to information about learning goals, learning activities and learners. A personalised recommender system has been developed accordingly, and recommends learners on the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed.This work has been sponsored by the EU project TENCompetenc

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field