Christine BauerParis Lodron University Salzburg · Department of Artificial Intelligence and Human Interfaces (AIHI)
Christine Bauer
Professor
About
180
Publications
148,305
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Introduction
My research activities center on interactive intelligent systems, where I integrate research on intelligent technologies, the interaction of humans with intelligent systems, and their interplay. Recently, I have focused on context-aware recommender systems and concentrate on recommender systems in the music and media sectors in particular.
You can find details and a full list of publications at https://christinebauer.eu
Additional affiliations
October 2024 - November 2024
Wissenschaft & Kunst
Position
- Co-Lead of the focus area
Description
- https://w-k.sbg.ac.at/en/intermediation-music-effect-analysis-2024-28/
September 2020 - April 2023
February 2020 - July 2020
Education
October 2006 - March 2011
October 2002 - July 2009
September 1998 - June 2000
Konservatorium der Stadt Wien
Field of study
- Jazz Saxophone
Publications
Publications (180)
As recommender systems play an important role in everyday life, there is an increasing pressure that such systems are fair. Besides serving diverse groups of users, recommenders need to represent and serve item providers fairly as well. In interviews with music artists, we identified that gender fairness is one of the artists' main concerns. They e...
Music streaming platforms are currently among the main sources of music consumption, and the embedded recommender systems significantly influence what the users consume. There is an increasing interest to ensure that those platforms and systems are fair. Yet, we first need to understand what fairness means in such a context. Although artists are th...
The comprehensive evaluation of the performance of a recommender system is a complex endeavor: many facets need to be considered in configuring an adequate and effective evaluation setting. Such facets include, for instance, defining the specific goals of the evaluation, choosing an evaluation method, underlying data, and suitable evaluation metric...
The diversity of the generated item suggestions can be an important quality factor of a recommender system. In offline experiments, diversity is commonly assessed with the help of the intra-list similarity (ILS) measure, which is defined as the average pairwise similarity of the items in a list. The similarity of each pair of items is often determi...
In the recommender systems field, it is increasingly recognized that focusing on accuracy measures is limiting and misguided. Unsurprisingly, in recent years, the field has witnessed more interest in the research of values "beyond accuracy." This trend is particularly pronounced in the news domain where recommender systems perform parts of the edit...
As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders. One particular concern in music recommendation is artist gender fairness. Recent work has shown that the gender imbalance in the sector translates to the output of music recommender systems, creating...
As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders. One particular concern in music recommendation is artist gender fairness. Recent work has shown that the gender imbalance in the sector translates to the output of music recommender systems, creating...
Recommender systems research and practice are fast-developing topics with growing adoption in a wide variety of information access scenarios. In this paper, we present an overview of research specifically focused on the evaluation of recommender systems. We perform a systematic literature review, in which we analyze 57 papers spanning six years (20...
Evaluation plays a vital role in recommender systems—in research and practice—whether for confirming algorithmic concepts or assessing the operational validity of designs and applications. It may span the evaluation of early ideas and approaches up to elaborate implementations of systems integrated into everyday product settings; it may target a wi...
This report documents the program and the outcomes of Dagstuhl Seminar 23031 "Frontiers of Information Access Experimentation for Research and Education", which brought together 38 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing) an...
The PhD Symposium was held successfully at the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023). A total of 22 people attended online or in person at the whole day event, which included two chairs, six mentors, nine students, and five panelists. Five people attended online, and 17 people attended in person. The...
Evaluation is a central step when developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2023 workshop, held as part of the 17th ACM Conference on Recommender Systems (RecSys 2023), served as a forum where researchers from both academia and industry critically reflected on the evaluation of recommender systems. The goal of the...
As streaming services have become a main channel for music consumption, they significantly impact various stakeholders: users, artists who provide music, and other professionals working in the music industry. Therefore, it is essential to consider all stakeholders' goals and values when developing and evaluating the music recommender systems integr...
Music recommender systems, commonly integrated into streaming services, help listeners find music. Previous research on such systems has focused on providing the best possible recommendations for these services' consumers, while others address fairness for artists that make their music available. While those insights are imperative, another group o...
As recommender systems are prone to various biases, bias mitiga-tion approaches are needed to counteract those. In the music sector, gender imbalance is a particular topical subject. Earlier work has shown that the gender imbalance in the sector translates to the output of music recommender systems. Several works emphasize that items representing w...
This report documents the program and the outcomes of Dagstuhl Seminar 23031 "Frontiers of Information Access Experimentation for Research and Education", which brought together 37 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, re-commender systems, natural language processing) a...
As users are increasingly confronted with information and choice overload, we need the 'right' information, at the 'right' time, in the 'right' place, in the 'right' way, to the `’right' person. Information retrieval and recommender systems are effective means to address this goal. When optimizing and evaluating such systems, we often disregard tha...
The majority of music consumption nowadays takes place on music streaming platforms. Whichever artists, albums, or songs are exposed to consumers on these platforms therefore greatly influences what music is ultimately consumed. As a result, the impact of these platforms on artists-their main item providers-is considerable. The recommender systems...
This panel aims to generate conversation toward creating a more equitable CHI. In recognizing our community's hard work thus far, this panel seeks to engage panelists and participants with thought-provoking questions to garner and promote actionable items for the community. We intend to have an open dialogue on allyship, diversity, equity, and incl...
This report documents the program and the outcomes of Dagstuhl Seminar 23031 ``Frontiers of Information Access Experimentation for Research and Education'', which brought together 37 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing)...
FairRecKit is a web-based analysis software that supports researchers in performing, analyzing, and understanding recommendation computations. The idea behind FairRecKit is to facilitate the in-depth analysis of recommendation outcomes considering fairness aspects. With (nested) filters on user or item attributes, metrics can easily be compared acr...
Various social influences affect group decision-making processes. For instance, individuals may adapt their behavior to fit in with the group's majority opinion. Furthermore, ingroup favoritism may lead individuals to favor the ideas of ingroup members rather than the outgroup. So far, little is explored on how these phenomena of social conformity...
Evaluation is a central step when it comes to developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2022 workshop at the 16th ACM Conference on Recommender Systems brought together academia and industry to critically reflect on the evaluation of recommender systems. The primary goal of the workshop was to capture the current s...
Evaluation of recommender systems is a central activity when developing recommender systems, both in industry and academia. The second edition of the PERSPECTIVES workshop held at Rec-Sys 2022 brought together academia and industry to critically reflect on the evaluation of recommender systems. In the 2022 edition of PERSPECTIVES, we discussed prob...
Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively little attention in this regard. However, addressing fairness of music recommender systems (MRSs) is highly important because the performance of these systems considerably im...
The performance of recommender systems highly impacts both music streaming platform users and the artists providing music. As fairness is a fundamental value of human life, there is increasing pressure for these algorithmic decision-making processes to be fair as well. However, many factors make recommender systems prone to biases, resulting in unf...
In group decision-making, we can frequently observe that an individual adapts their behavior or belief to fit in with the group’s majority opinion. This phenomenon has been widely observed to exist especially against an objectively correct answer—in face-to-face and online interaction alike. To a lesser extent, studies have investigated the conform...
Abstract: In this article, we explore explicit gendering in the manner in which voices are treated in music and audio and whether this relates to the specific function of the voice in a given context. Building on existing work on gender in singing, we explore the ways in which the voice is gendered through the use of voice production software. Spec...
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...
Evaluation is a central step when it comes to developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2021 workshop at the 15th ACM Conference on Recommender Systems brought together academia and industry to critically reflect on the evaluation of recommender systems. The primary goal of the workshop was to capture the current s...
Evaluation is a cornerstone in the process of developing and deploying recommender systems. The PERSPECTIVES workshop brought together academia and industry to critically reflect on the evaluation of recommender systems. Particularly, the workshop aimed to shed light on the different, and maybe even diverging or contradictory perspectives on the ev...
Music streaming platforms are currently among the main sources of music consumption, and the embedded recommender systems significantly influence what the users consume. There is an increasing interest to ensure that those platforms and systems are fair. Yet, we first need to understand what fairness means in such a context. Although artists are th...
When evaluating personalized or adaptive systems, we frequently rely on one single evaluation objective and one single method. This remains us with “blind spots”. A comprehensive evaluation may require a thoughtful integration of multiple methods. This tutorial (i) demonstrates the wide variety of dimensions to be eval- uated, (ii) outlines the met...
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...
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...
Recommender systems are among today’s most successful application areas of AI. However, in the recommender systems research community, we have fallen prey of a McNamara fallacy to a worrying extent: In the majority of our research efforts, we rely almost exclusively on computational measures such as prediction accuracy, which are easier to make tha...
The 21st edition of the Annual Conference of the International Society for Music Information Retrieval (ISMIR) introduced so-called "special sessions," giving room for discussion on various topics related to music information retrieval (MIR). I report on the activities related to this special session No. 7 with the title "How do we---in MIR researc...
Among the many viable research questions in the field of recom-mender systems, a frequently addressed problem is to accurately predict the relevance of individual items to users, with the goal of presenting the assumedly most relevant ones as recommendations. Typically, we have users' (explicit or implicit) ratings as input and rankings of items as...
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...
A strong research record has evidenced that individuals tend to conform with a group's majority opinion. In contrast to existing literature that investigates conformity to a majority opinion against an objectively correct answer, the originality of our study lies in that we investigate conformity in a subjective context. The emphasis of our analysi...
The analysis of consumers' personal information (PI) is a significant source to learn about consumers. In online settings, many consumers disclose PI abundantly -- this is particularly true for information provided on social network services. Still, people manage the privacy level they want to maintain by disclosing by disclosing PI accordingly. In...
Research in the field of information retrieval and recommendation mostly focuses on one single evaluation method and one single quality objective. On the one hand, many research endeavors focus on system-centric evaluation from an algorithmic perspective and consider the context of use only to a minor extent. On the other hand, there are research e...
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...
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...
In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not able to evaluate all relevant aspects in such a complex setting. We reason that employing a multi-method evaluation, where multiple evaluation methods or measures are co...
The task of a music recommender system is to predict what music item a particular user would like to listen to next. This position paper discusses the main challenges of the music preference prediction task: the lack of information on the many contextual factors influencing a user's music preferences in existing open datasets, the lack of clarity o...
Socio-demographic user profiles are currently regarded as the most convenient base for successful personalized advertising. However, signs point to the dormant power of context recognition. While technologies that can sense the environment are increasingly advanced, questions such as what to sense and how to adapt to a consumer's context are largel...
Promoting diversity in the music sector is widely discussed on the media. While the major problem may lie deep in our society, music information retrieval contributes to promoting diversity or may create unequal opportunities for artists. For example, considering the known problem of popularity bias in music recommendation, it is important to inves...
Promoting diversity in the music sector is widely discussed on the media. While the major problem may lie deep in our society, music information retrieval contributes to promoting diversity or may create unequal opportunities for artists. For example, considering the known problem of popularity bias in music recommendation, it is important to inves...
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...
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...
In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not able to evaluate all relevant aspects in such a complex setting. We reason that employing a multi-method evaluation, where multiple evaluation methods or measures are co...
In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not able to evalu- ate all relevant aspects in such a complex setting. We reason that employing a multi-method evaluation, where multiple evaluation methods or measures are...
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 m...
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...
The task of a music recommender system is to predict what music item a particular user would like to listen to next. This position paper discusses the main challenges of the music preference prediction task: the lack of information on the many contextual factors influencing a user’s music preferences in existing open datasets, the lack of clarity o...
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...
Given the global expansion, the borderless nature, and the social impact of social media, this paper provides an examination of users' connection patterns in online social networks, more specifically the users' crosscountry connection patterns. We study three highly different social media platforms, Facebook, Last.fm, and 500px, and approach two ma...
The emergence and on-going development of digital signage (DS) systems result in a growing number of technological capabilities of such systems. While these technological capabilities have attracted considerable research attention in informatics, studies exploring their application and impact are scarce. Marketing, and especially retailing, represe...
We investigate the complex relationship between the factors (i) preference for music mainstream, (ii) social ties in an online music platform, and (iii) demographics. We define (i) on a global and a country level, (ii) by several network centrality measures such as Jaccard index among users' connections, closeness centrality, and betweenness centra...
The music mainstreaminess of a listener reflects how strong a person’s listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives, we show country-specific differences and study how taking into account music mainstreaminess influences the quality of music recommen...
We approach the research question whether real-world events, such as sport events or product launches, influence music consumption behavior. To this end, we consider events of different categories from Google Trends and model listening events as time series using Last.fm data. Performing an auto-regressive integrated moving average analysis to deco...
The music mainstreaminess of a listener reflects how strong a person’s listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives, we show country-specific differences and study how taking into account music mainstreaminess influences the quality of music recommen...
In the field of music recommender systems, country-specific aspects have received little attention, although it is known that music perception and preferences are shaped by culture; and culture varies across countries. Based on the LFM-1b dataset (including 53,258 users from 47 countries), we show that there are significant country-specific differe...
The analysis of consumers' personal information (PI) is a significant source to learn about consumers. In online settings, many consumers disclose PI abundantly-this is particularly true for information provided on social network services. Still, people manage the privacy level they want to maintain by disclosing by disclosing PI accordingly. In ad...
Dieser Artikel widmet sich der Perspektive der Informatik in der Musikwirtschaftsforschung. Zunächst wird der Erkenntnisgegenstand der Musikwirtschaftsforschung aus dieser Perspektive dargelegt und das zur Verfügung stehende Methodeninstrumentarium aufgezeigt. Dabei untermauert diese Arbeit, dass die Perspektive der Informatik in der Musikwirtschaf...
The music mainstreaminess of a user reflects how strong a user's listening preferences correspond to those of the larger population. Considering that music mainstream may be defined from different perspectives and on various levels, e.g., geographical (charts of a country), genre ("Indie charts"), or distribution channel (radio charts vs. download...
At the heart of ubiquitous and pervasive computing is the integration of semantically rich contextual information into systems that intelligently adapt their behavior to the context. This paper presents an analysis of the contextual elements considered in the scientific discourse on pervasive computing. To support researchers with positioning their...
Ubiquitous business processes are the new generation of processes that pervade the physical space and interact with their environments using a minimum of human involvement. Although they are now widely deployed in the industry, their deployment is still ad hoc. They are implemented after an arbitrary modeling phase or no modeling phase at all. The...
Due to the high potential of digital media to support learning processes and outcomes, educational games have gained wide acceptance over the years. The combination of mobile devices with location-based technologies offers new options and possibilities for the development of educational games in consideration of learners' environment with the posit...
Due to the high potential of digital media to support learning processes and outcomes, educational games have gained wide acceptance over the years. The combination of mobile devices with location-based technologies offers new options and possibilities for the development of educational games in consideration of learners’ environment with the posit...
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 pref- erences of this young user group and analyze these preferences for homogeneity within more fine-grained age groups and with respect to gender and c...
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...
ere is a long tradition in recommender systems research to eval- uate systems using quantitative performance measures on xed datasets. As a reaction to this narrow accuracy-based focus in research, novel qualities beyond pure accuracy are emphasized in recent research; among them are surprise and opposition.
is position paper considers that the per...
A music listener's mainstreaminess indicates the extent to which her listening preferences correspond to those of the population at large.
However, formal definitions to quantify the level of mainstreaminess of a listener are rare and those available define mainstreaminess based on fractions between some kind of individual and global listening pro...