• On the design of consequential ranking algorithms

  Tabibian, Behzad; Gómez, Vicenç; De, Abir; Schölkopf, Bernhard; Gomez-Rodriguez, Manuel (Proceedings of Machine Learning Research, 2020)

  Ranking models are typically designed to optimize some measure of immediate utility to the users. As a result, they have been unable to anticipate an increasing number of undesirable long-term consequences of their proposed ...
• Skip prediction using boosting trees based on acoustic features of tracks in sessions

  Ferraro, Andrés; Bogdanov, Dmitry; Serra, Xavier (2019)

  The Spotify Sequential Skip Prediction Challenge focuses on predicting if a track in a session will be skipped by the user or not. In this paper, we describe our approach to this problem and the final system that was ...
• How are learning analytics considering the societal values of fairness, accountability, transparency and human well-being?: a literature review

  Hakami, Eyad; Hernández Leo, Davinia (CEUR Workshop Proceedings, 2020)

  The scientific community is currently engaged in global efforts towards a movement that promotes positive human values in the ways we formulate and apply Artificial Intelligence (AI) solutions. As the use of intelligent ...
• Towards reward-based gamification in collaborative learning flow patterns based on learning analytics

  Lobo Quintero, René; Ortega Arranz, Alejandro; Hernández Leo, Davinia (CEUR Workshop Proceedings, 2020)

  Collaborative learning flow patterns (CLFPs) formulate good practices for the orchestration of activity sequences and collaboration mechanisms that can elicit fruitful social interactions. Despite their benefits, it is ...
• End-to-end music source separation: is it possible in the waveform domain?

  Lluís, Francesc; Pons, Jordi; Serra, Xavier (International Speech Communication Association (ISCA), 2019)

  Most of the currently successful source separation techniques use the magnitude spectrogram as input, and are therefore by default omitting part of the signal: the phase. To avoid omitting potentially useful information, ...
• What kind of content are you prone to tweet?: multi-topic preference model for tweeters

  Recalde, Lorena; Baeza Yates, Ricardo (Springer, 2020)

  According to tastes, a person could show preference for a given category of content to a greater or lesser extent. However, quantifying people’s amount of interest in a certain topic is a challenging task, especially ...
• Updateable inner product argument with logarithmic verifier and applications

  Daza, Vanesa; Ràfols, Carla; Zacharakis, Alexandros (Springer, 2020)

  We propose an improvement for the inner product argument of Bootle et al. (EUROCRYPT’16). The new argument replaces the unstructured common reference string (the commitment key) by a structured one. We give two instantiations ...
• Distributed retrieval practice enhances primary school students’ retention of computational thinking concepts

  Casanova De Vilalta, Lydia; Theophilou, Emily; Beardsley, Marc; Santos Rodríguez, Patrícia; Hernández Leo, Davinia (IADIS Press, 2020)

  Efforts are being made to add Computational Thinking (CT) to the curriculum of primary schools in order to develop student abilities to use computational tools to solve problems and better prepare them for an increasingly ...
• Biases on social media data: (keynote extended abstract)

  Baeza Yates, Ricardo (ACM Association for Computer Machinery, 2020)

  Social media data is often used to pulse the opinion of online communities, either by predicting sentiment or stances (e.g., political), to mention just two typical use cases. However, those analysis assume that the data ...
• On security policy migrations

  Lobo, Jorge; Bertino, Elisa; Russo, Alessandra (ACM Association for Computer Machinery, 2020)

  There has been over the past decade a rapid change towards computational environments that are comprised of large and diverse sets of devices, many of them mobile, which can connect in flexible and context-dependent ways. ...
• FairSearch: a tool for fairness in ranked search results

  Zehlike, Meike; Sühr, Tom; Castillo, Carlos; Kitanovski, Ivan (ACM Association for Computer Machinery, 2020)

  Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, ...
• Taking advantage of editorial metadata to recommend music

  Bogdanov, Dmitry; Herrera Boyer, Perfecto, 1964- (2012)

  In this work we propose a novel approach to music recommendation based exclusively on editorial metadata. To this end, we propose to use a public database of music releases Discogs.com, which contains extensive information ...
• Rethinking social innovations for active and healthy ageing within ICT research and innovation programs in Europe: beyond health, more older people-centric, and technology adoption

  Sayago Barrantes, Sergio; Righi, Valeria; Rosales, Andrea; Möller Ferreira, Susan; Blat, Josep (2016)

  By drawing upon slightly more than a decade of research in the field of Human-Computer Interaction / ICT & Ageing, conducted by the authors in adult educational centres and computer clubhouses in European and South American ...
• How much metadata do we need in music recommendation?: a subjective evaluation using preference sets

  Bogdanov, Dmitry; Herrera Boyer, Perfecto, 1964- (International Society for Music Information Retrieval (ISMIR), 2011)

  In this work we consider distance-based approaches to music recommendation, relying on an explicit set of music tracks provided by the user as evidence of his/her music preferences. Firstly, we propose a purely content-based ...
• Music retagging using label propagation and robust principal component analysis

  Yang, Yi-Hsuan; Bogdanov, Dmitry; Herrera Boyer, Perfecto, 1964-; Sordo, Mohamed (ACM Association for Computer Machinery, 2012)

  The emergence of social tagging websites such as Last.fm has provided new opportunities for learning computational models that automatically tag music. Researchers typically obtain music tags from the Internet and use them ...
• From low-level to high-level: comparative study of music similarity

  Bogdanov, Dmitry; Serrà Julià, Joan; Wack, Nicolas; Herrera Boyer, Perfecto, 1964- (Institute of Electrical and Electronics Engineers (IEEE), 2009)

  Studying the ways to recommend music to a user is a central task within the music information research community. From a content-based point of view, this task can be regarded as obtaining a suitable distance measurement ...
• The Musical Avatar: a visualization of musical preferences by means of audio content description

  Haro Berois, Martín; Xambó, Anna; Fuhrmann, Ferdinand; Bogdanov, Dmitry; Gómez Gutiérrez, Emilia, 1975-; Herrera Boyer, Perfecto, 1964- (ACM Association for Computer Machinery, 2010)

  The music we like (i.e. our musical preferences) encodes and communicates key information about ourselves. Depicting such preferences in a condensed and easily understandable way is very appealing, especially considering ...
• Content-based music recommendation based on user preference examples

  Bogdanov, Dmitry; Haro Berois, Martín; Fuhrmann, Ferdinand; Gómez Gutiérrez, Emilia, 1975-; Herrera Boyer, Perfecto, 1964- (CEUR Workshop Proceedings, 2010)

  Recommending relevant and novel music to a user is one of the central applied problems in music information research. In the present work we propose three content-based approaches to this task. Starting from an explicit ...
• A Content-based system for music recommendation and visualization of user preferences working on semantic notions

  Bogdanov, Dmitry; Haro Berois, Martín; Fuhrmann, Ferdinand; Xambó, Anna; Gómez Gutiérrez, Emilia, 1975-; Herrera Boyer, Perfecto, 1964- (Institute of Electrical and Electronics Engineers (IEEE), 2011)

  The amount of digital music has grown unprecedentedly during the last years and requires the development of effective methods for search and retrieval. In particular, contentbased preference elicitation for music recommendation ...
• Robust facial alignment with internal denoising auto-encoder

  Aspandi, Decky; Martínez, Oriol; Sukno, Federico Mateo; Binefa i Valls, Xavier (Institute of Electrical and Electronics Engineers (IEEE), 2019)

  The development of facial alignment models is growing rapidly thanks to the availability of large facial landmarked datasets and powerful deep learning models. However, important challenges still remain for facial alignment ...

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