Markus Schedl

Markus Schedl
Johannes Kepler University Linz | JKU · Institute of Computational Perception

D.I. Mag. Dr.

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299
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Publications

Publications (299)
Article
The most common way to listen to recorded music nowadays is via streaming platforms, which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of music recommender systems (MRSs) has become essential. Current real‐world MRSs are often quite complex and optimized for recommendat...
Preprint
Full-text available
User-based KNN recommender systems (UserKNN) utilize the rating data of a target user's k nearest neighbors in the recommendation process. This, however, increases the privacy risk of the neighbors since their rating data might be exposed to other users or malicious parties. To reduce this risk, existing work applies differential privacy by adding...
Preprint
Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, race, and location. These encoded biases can influence the decision of a recommendation system (RS) towards further separation of the contents provided to various demograp...
Chapter
This chapter studies state-of-the-art research related to multimedia recommender systems (MMRS), focusing on methods that integrate multimedia content as side information to various recommendation models. The multimedia features are then used by an MMRS to recommend either (1) media items from which the features were derived, or (2) non-media items...
Preprint
This work investigates the effect of gender-stereotypical biases in the content of retrieved results on the relevance judgement of users/annotators. In particular, since relevance in information retrieval (IR) is a multi-dimensional concept, we study whether the value and quality of the retrieved documents for some bias-sensitive queries can be jud...
Preprint
Full-text available
The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendati...
Preprint
Full-text available
The results of information retrieval (IR) systems on specific queries can reflect the existing societal biases and stereotypes, which will be further propagated and straightened through interactions of the uses with the systems. We introduce Grep-BiasIR, a novel thoroughly-audited dataset which aim to facilitate the studies of gender bias in the re...
Article
Full-text available
Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the main...
Conference Paper
Full-text available
Renaissance music constitutes a resource of immense richness for Western culture, as shown by its central role in digital humanities. Yet, despite the advance of computational musicology in analysing other Western repertoires, the use of computer-based methods to automatically retrieve relevant information from Renaissance music, e. g., identifying...
Conference Paper
Full-text available
The extent to which the sequence of tracks in music playlists matters to listeners is a disputed question, nevertheless a very important one for tasks such as music recommendation (e. g., automatic playlist generation or continuation). While several user studies already approached this question, results are largely inconsistent. In contrast, in thi...
Preprint
Full-text available
Homophily describes the phenomenon that similarity breeds connection, i.e., individuals tend to form ties with other people who are similar to themselves in some aspect(s). The similarity in music taste can undoubtedly influence who we make friends with and shape our social circles. In this paper, we study homophily in an online music platform Last...
Article
Although recommender systems (RSs) play a crucial role in our society, previous studies have revealed that the performance of RSs may considerably differ between groups of individuals with different characteristics or from different demographics. In this case, a RS is considered to be unfair when it does not perform equally well for different group...
Preprint
Full-text available
Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing work predominantly adopts simple statistical measures, such as the difference of mean or median p...
Preprint
Full-text available
Providing suitable recommendations is of vital importance to improve the user satisfaction of music recommender systems. Here, users often listen to the same track repeatedly and appreciate recommendations of the same song multiple times. Thus, accounting for users' relistening behavior is critical for music recommender systems. In this paper, we d...
Preprint
Full-text available
The 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 levera...
Conference Paper
Societal biases resonate in the retrieved contents of informationretrieval (IR) systems, resulting in reinforcing existing stereotypes.Approaching this issue requires established measures of fairness inrespect to the representation of various social groups in retrieval re-sults, as well as methods to mitigate such biases, particularly in thelight o...
Preprint
Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich literature of classical generative retrieval models, we introduce and formalize the paradigm of deep generative retrieval models defined via the cumulative probabilities of gen...
Preprint
Full-text available
Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and creators. The great strides in search and recommendation in research and industry have yet to see impact in the podcast space, where recommendations are still largely driv...
Preprint
Societal biases resonate in the retrieved contents of information retrieval (IR) systems, resulting in reinforcing existing stereotypes. Approaching this issue requires established measures of fairness regarding the representation of various social groups in retrieved contents, as well as methods to mitigate such biases, particularly in the light o...
Chapter
Although current music recommender systems suggest new tracks to their users, they do not provide listenable explanations of why a user should listen to them. LEMONS (Demonstration video: https://youtu.be/giSPrPnZ7mc) is a new system that addresses this gap by (1) adopting a deep learning approach to generate audio content-based recommendations fro...
Preprint
Click logs are valuable resources for a variety of information retrieval (IR) tasks. This includes query understanding/analysis, as well as learning effective IR models particularly when the models require large amounts of training data. We release a large-scale domain-specific dataset of click logs, obtained from user interactions of the Trip Data...
Preprint
Full-text available
Music recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the main...
Article
Full-text available
Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what co...
Book
Full-text available
Personalized recommender systems have become indispensable in today’s online world. Most of today’s recommendation algorithms are data-driven and based on behavioral data. While such systems can produce useful recommendations, they are often uninterpretable, black-box models that do not incorporate the underlying cognitive reasons for user behavior...
Conference Paper
The COVID-19 pandemic causes a massive global health crisis and produces substantial economic and social distress , which in turn may cause stress and anxiety among people. Real-world events play a key role in shaping collective sentiment in a society. As people listen to music daily everywhere in the world, the sentiment of music being listened to...
Conference Paper
Full-text available
While many researchers have proposed various ways of quantifying recommendation list diversity, these approaches have had little input from users on their own perceptions and preferences in seeking diversity. Through an exploratory user study, we provide a better understanding of how users view the concept of diversity in music recommendations , an...
Preprint
Full-text available
Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what co...
Article
Full-text available
Recommender systems have become a popular and effective means to manage the ever-increasing amount of multimedia content available today and to help users discover interesting new items. Today's recommender systems suggest items of various media types, including audio, text, visual (images), and videos. In fact, scientific research related to the a...
Article
Full-text available
In this paper, we report on the creation of a publicly available, common evaluation framework, for Violent Scenes Detection (VSD) in Hollywood and YouTube videos. We propose a robust data set, the VSD96, with more than 96 hours of video of various genres, annotations at different levels of detail (e.g., shot-level, segment-level), annotations of mi...
Chapter
Full-text available
Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-tail items as well as users wi...
Article
Full-text available
In this paper, we address the problem of modeling and predicting the music genre preferences of users. We introduce a novel user modeling approach, 'BLLu', which takes into account the popularity of music genres as well as temporal drifts of user listening behavior. To model these two factors, 'BLLu' adopts a psychological model that describes how...
Preprint
Full-text available
In this paper, we introduce a psychology-inspired approach to model and predict the music genre preferences of different groups of users by utilizing human memory processes. These processes describe how humans access information units in their memory by considering the factors of (i) past usage frequency, (ii) past usage recency, and (iii) the curr...
Article
Full-text available
Integrating information about the listener’s cultural background when building music recommender systems has recently been identified as a means to improve recommendation quality. In this article, we, therefore, propose a novel approach to jointly model users by their 'musical preferences' and 'cultural backgrounds'. We describe the musical prefere...
Preprint
Full-text available
In this paper, we analyze a large dataset of user-generated music listening events from Last.fm, focusing on users aged 6 to 18 years. Our contribution is two-fold. First, we study the music genre preferences of this young user group and analyze these preferences for homogeneity within more fine-grained age groups and with respect to gender and cou...
Preprint
Full-text available
Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to music recommendation fall short in satisfying listeners that have specialized music. The contribution of this arti...
Preprint
Full-text available
This 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...
Conference Paper
Full-text available
The MediaEval 2019 Task "Multimedia for Recommender Systems" investigates the potential of leveraging multimedia content to enhance recommender systems. In this task, participants use a wealth of information from text, images, and audio to predict the success of items. Thereby, we advance the state-of-the-art of content-based recommender systems by...
Poster
Full-text available
We present a browsing interface that allows for an audiovisual exploration of regional music taste around the world. We exploit a total of 10,758,121 geolocated tweets about music. The web-based geo-aware visualization and auralization called Tastalyzer enables exploring and analyzing music taste on a fine-grained geographical level, such as (i) co...
Conference Paper
Full-text available
We present a browsing interface that allows for an audiovisual exploration of regional music taste around the world. We exploit a total of 10,758,121 geolocated tweets about music. The web-based geo-aware visualization and auralization called Tastalyzer enables exploring and analyzing music taste on a fine-grained geographical level, such as (i) co...
Chapter
Music recommender systems are a widely adopted application of personalized systems and interfaces. By tracking the listening activity of their users and building preference profiles, a user can be given recommendations based on the preference profiles of all users (collaborative filtering), characteristics of the music listened to (content-based me...
Preprint
Full-text available
In order to improve the accuracy of recommendations, many recommender systems nowadays use side information beyond the user rating matrix, such as item content. These systems build user profiles as estimates of users' interest on content (e.g., movie genre, director or cast) and then evaluate the performance of the recommender system as a whole e.g...
Preprint
Full-text available
In order to improve the accuracy of recommendations, many recommender systems nowadays use side information beyond the user rating matrix, such as item content. These systems build user profiles as estimates of users' interest on content (e.g., movie genre, director or cast) and then evaluate the performance of the recommender system as a whole e.g...
Article
The ACM Recommender Systems Challenge 2018 focused on the task of automatic music playlist continuation, which is a form of the more general task of sequential recommendation. Given a playlist of arbitrary length with some additional meta-data, the task was to recommend up to 500 tracks that fit the target characteristics of the original playlist....
Conference Paper
Full-text available
We present a demo application of a web-based recommender systems that is powered by the "Movie Genome", i.e., a rich semantic description of a movie's content, including state-of-the-art audio and visual descriptors and metadata (genre and tags). The current version of the Movie Genome web application implements content-based filtering approaches....
Conference Paper
Full-text available
Multimedia search is an emerging area in information retrieval (IR) and recommender systems (RS) research. However, there is a lack of standardized audiovisual datasets that include rich content descriptors, which are a necessity in content-based IR and RS. The contributions of this paper are twofold: First, we present a new multimedia dataset of m...
Article
Full-text available
Like in many other research areas, deep learning (DL) is increasingly adopted in music recommendation systems (MRS). Deep neural networks are used in this domain particularly for extracting latent factors of music items from audio signals or metadata and for learning sequential patterns of music items (tracks or artists) from music playlists or lis...
Preprint
Full-text available
Music recommender systems have become central parts of popular streaming platforms such as Last.fm, Pandora, or Spotify to help users find music that fits their preferences. These systems learn from the past listening events of users to recommend music a user will likely listen to in the future. Here, current algorithms typically employ collaborati...
Article
Full-text available
Music streaming services increasingly incorporate different ways for users to browse for music. Next to the commonly used “genre” taxonomy, nowadays additional taxonomies, such as mood and activities, are often used. As additional taxonomies have shown to be able to distract the user in their search, we looked at how to predict taxonomy preferences...
Article
Full-text available
Relevance: Popularity-based approaches are widely adopted in music recommendation systems, both in industry and research. These approaches recommend to the target user what is currently popular among all users of the system. However, as the popularity distribution of music items typically is a long-tail distribution, popularity-based approaches to...
Article
Full-text available
The availability of increasingly larger multimedia collections has fostered extensive research in recommender systems. Instead of capturing general user preferences, the task of next-item recommendation focuses on revealing specific session preferences encoded in the most recent user interactions. This study focuses on the music domain, particularl...
Conference Paper
Music is known to exhibit different characteristics, depending on genre and style. While most research that studies such differences takes a musicological perspective and analyzes acoustic properties of individual pieces or artists, we conduct a large-scale analysis using various web resources. Exploiting content information from song lyrics, conte...
Poster
Full-text available
Social connections and cultural aspects play important roles in shaping an individual's preferences. For instance, people tend to select friends with similar music preferences. Furthermore, preferences and friending are influenced by cultural aspects. Recommender systems may benefit from these phenomena by using knowledge about the nature of social...
Conference Paper
Full-text available
Social connections and cultural aspects play important roles in shaping an individual's preferences. For instance, people tend to select friends with similar music preferences. Furthermore, preferences and friending are influenced by cultural aspects. Recommender systems may benefit from these phenomena by using knowledge about the nature of social...
Article
Full-text available
As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a s...