1,721,025 research outputs found
A regression approach to movie rating prediction using multimedia content and metadata
No full textThis paper presents the submission of the team MASlab-ZNU to the MMRecSys movie recommendation task, as part of MediaEval 2019. The task involved predicting average movie ratings, standard deviation of ratings, and the number of ratings by using audio and visual features extracted from trailers and the associated metadata. In the proposed work, we model the rating prediction problem as a regression problem and employ different learning models for the prediction task, including ridge regression (RR), support vector regression (SVR), shallow neural network (SNN) and deep neural network (DNN). The results of fairly large amount of experiments on various models and features indicate that combination of DNN+tag features produce the best results for prediction of avgRating and StdRating while for numRating (popularity) it is the combination of RR+tag that significantly outperforms the other competitors, with a large margin
Trustworthy User Modeling and Recommendation from Technical and Regulatory Perspectives
No full textThis tutorial provides an interdisciplinary overview of fairness, non-discrimination, transparency, privacy, and security in the context of recommender systems. According to European policies, these are essential dimensions of trustworthy AI systems but also extend to the global debate on regulating AI technology. Since the aspects mentioned earlier require more than technical considerations, we discuss these topics from ethical, legal, and regulatory perspectives. While the tutorial's primary focus is on presenting technical solutions that address the mentioned topics of trustworthiness, it also equips the primarily technical audience of UMAP with the necessary understanding of the social and ethical implications of their research and development and recent ethical guidelines and regulatory frameworks
A Professionally Annotated and Enriched Multimodal Data Set on Popular Music
No full textThis paper presents the MusiClef data set, a multimodal data set of professionally annotated music. It includes
editorial meta-data about songs, albums, and artists, as well as MusicBrainz identifiers to facilitate linking to other data sets. In addition, several audio features (generic low-level descriptors and state-of-the-art music features) are provided. Different sets of annotations as well as music context data – collaboratively generated user tags, web pages about artists and albums, and the annotation labels provided by music experts – are included too. Versions of this data set were used
in the MusiCLEF 2011 and in the MusiClef 2012 evaluation campaigns for auto-tagging tasks
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Content-driven music recommendation: Evolution, state of the art, and challenges
No full textThe music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that leverage collaborative and content data – which we refer to as content-driven models – have been replacing pure CF or CB models. In this survey, we review 55 articles on content-driven music recommendation. Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated content, and derivative content. We provide a detailed characterization of each category along several dimensions. Second, we identify six overarching challenges, according to which we organize our main discussion: increasing recommendation diversity and novelty, providing transparency and explanations, accomplishing context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start. Each article addresses one or more of these challenges and is categorized according to the content layers of our onion model, the article's goal(s), and main methodological choices. Furthermore, articles are discussed in temporal order to shed light on the evolution of content-driven music recommendation strategies. Finally, we provide our personal selection of the persisting grand challenges which are still waiting to be solved in future research endeavors
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
MusiClef: Multimodal Music Tagging Task
No full textMusiClef is a multimodal music benchmarking initiative that will be running a MediaEval 2012 Brave New Task on Multimodal Music Tagging. This paper describes the setup of this task, showing how it complements existing benchmarking initiatives and fosters less explored methodological directions in Music Information Retrieval. MusiClef deals with a concrete use case, encourages multimodal approaches based on these, and strives for transparency of results as much as possible. Transparency is encouraged at several levels and stages, from the feature extraction procedure up to the evaluation phase, in which a dedicated categorization of ground truth tags will be used to deepen the understanding of the relation between the proposed approaches and experimental results
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