Before the era of the Internet, access to music content (e.g., music recordings) was restricted to local availability of their physical representations (e.g., vinyl). Thereby, the selection and aggregation of content had traditionally been exposed to human control (Oechslein and Hess, 2014). For instance, a small group of Artist&Repertoire managers working for the major music labels scouted new artists and developed them commercially.
Nowadays,—owing to the development of the Social Web that allows for easy distribution of user-generated content—the intermediary level of experts (e.g., the Artist&Repertoire managers at music labels) that traditionally “prefiltered” content before it reached potential consumers is bypassed. This results in the situation that users currently face: music content is abundantly available online and the amount of overall available content increases tremendously on a daily basis.
amples of differences associated with music items, users, and their consumption behaviour include the following(Schedl et al., 2015):
very low consumption time in the dimension of minutes, whereas a book or a travel are consumed during days or weeks;
consumption in sequences (e.g., playlists);
music often consumed passively (e.g., while jogging, travelling, working);
consumption is highly driven by situational context;
users are likely to appreciate the re-recommendation of the same item while a user is less likely to read the same news article over and over again; and
music evokes strong emotions.
However, the opportunity to access a large amount of content frequently leads to information overload (Bawden and Robinson, 2009) or choice overload (Häubl and Trifts, 2000), because people do not find the content that they are interested in or do not know what to choose. Assisting users in searching, sorting, and filtering the massive amount of online content (Montaner et al., 2003), recommender systems have become important tools in people’s everyday life and do not only facilitate the interaction with music content (Celma, 2010), but also support versatile activities such as shopping (Oestreicher-Singer and Sundararajan, 2012), consuming news (Oechslein and Hess, 2014), or finding persons for any kind of social matching (Mayer et al., 2015).
Recommender systems are computer systems that provide suggestions for items that are deemed interesting to a particular target user, assisting that particular user in various decision-making processes (e.g., relating to what music to listen to) (Ricci et al., 2015). The general term used to denote to what the system recommends to users is “item” (Ricci et al., 2015); in case of music recommender systems (MRS) it is the music item (e.g., musical work, artist, genre).
There are universally valid principles for designing recommender systems, such as that a recommender system typically consist of three key components (i.e., user, item, and matching mechanism) (Bauer et al., 2017). Still, a recommender system needs to be put into context because there are product- and sector-specific characteristics that a recommender system needs to consider (be customized to) to provide useful and effective recommendations for the specific type of item (Ricci et al., 2015; Schedl et al., 2014). Sidebar 1 presents the specialties of the music domain compared to other domains deploying recommender systems.
An ideal MRS proposes “the right music, to the right user, at the right moment”(Laplante, 2014). However, this is a complex task because various factors influence a user’s music preferences in a given situation (Bauer and Schedl, 2018b). Many studies have investigated the relationships between music preferences and various person-related characteristics (e.g., demographics (Cheng et al., 2017), personality traits (Schäfer and Mehlhorn, 2017), social influences (Bonneville-Roussy and Rust, 2018). Besides person-related characteristics, also situation-related factors (e.g., temporal aspects (Dias and Fonseca, 2013), or weather (Braunhofer et al., 2011)) influence a user’s music preferences. The task of an MRS is to predict what a particular user would like to listen to next. Basically, there are two computational modeling approaches to build upon for this music preference prediction task:
Theoretical modeling seeks to explain users’ listening behavior. For advancing MRS, the first step would be to observe a user’s listening behavior and perform analyses to explain where a user’s listening behavior results from (e.g., from person-related characteristics or situational factors, and from which of these in particular). Then, building on these findings (e.g., knowing that Finnish listeners are more likely to prefer heavy metal than Italian listeners (Schedl, 2017)), future user models may be created for predictions.
Algorithmic modeling seeks to predict users’ listening behavior. Algorithmic modeling may rely on approaches that are capable of identifying listening patterns within a user’s listening history or across users without necessarily delivering descriptions that help explaining
the relationships of the identified patterns. For instance, approaches such as deep neural networks frequently leave us with “black boxes”(Koh and Liang, 2017) because the resulting models are complex and frequently they do not produce an intelligible description of the results produced in each case. Still, the resulting models may be apt to deliver remarkably accurate predictions. In other words, algorithmic modeling may recommend music to the user what he or she will indeed like in the very moment without understanding whether it was indeed the “right” choice—and if—why it was “right”.
One challenge for music preference prediction is that it is (almost) impossible to say what is the right choice for a particular user in the particular moment; it is typically a set of items that is right or okay.
Another challenge of algorithmic modeling is that—currently—we can only model based on data that we have available. For MRS, several open datasets exist, such as the Million Song Dataset (Bertin-Mahieux et al., 2011), the LFM-1b dataset (Schedl, 2016), or the recently released Music Streaming Sessions Dataset (Brost et al., 2019). However, there are many factors influencing a user’s music preferences for which we do not have (sufficient) data available (yet) to exploit for algorithmic modeling. Theoretical modeling—thus, the “explaining approach”—may help here to advance MRS. It is also a viable basis to provide an informed route what kind of data should be collected so that algorithmic modeling may come into play here to use its powerful mechanisms to exploit the additional data to make even better predictions.
A further challenge relates to evaluation of MRS: What does it mean if an MRS recommends a music item to a user and the user indeed listens to the item? Potentially, it is the user’s most favorite song and so the user enjoyed listening to it. Maybe, though, the user listens to the item because the algorithm provided it as the next one to listen to in the playlist, but the user was distracted at the very moment because of receiving a phone call (or was not present in the room for some minutes). In such cases, the recommendation was maybe not a “bad” one because the user did not hear it anyways, but was it a good prediction then?
With respect to biases as inherent in recommendation systems (e.g., the popularity bias phenomenon (Celma and Cano, 2008) suggesting that over time the most popular music items tend to get more and more attention, while music items in the long tail get less and less attention (Levy and Bosteels, 2010)), the ability to understand and explain models seems to be a crucial prerequisite to uncover such bias and develop and take effective measures to counteract unwanted bias.
4. Previous and Ongoing Research, and Interests
A major part of my previous and ongoing research is aimed at integrating contextual information into (user) modeling. Basically, my work on context modeling takes a conceptual viewpoint (e.g., (Bauer and Novotny, 2017; Bauer, 2014)). It points towards the various potentially relevant contextual factors that we tend to “forget” in modeling (for various reasons such as, for instance, the non-availability of useful datasets including such contextual information).
With the main objective at improving MRS, some part of my research on MRS is geared towards identifying relationships between various aspects (such as age (Schedl and Bauer, 2017), user connections (Bauer and Schedl, 2019, 2018a), user country (Bauer and Schedl, 2018b), real-world events (Schedl et al., Conference Proceedings), mainstreaminess (Schedl and Bauer, 2017)) and music preferences or listening behavior. Findings are then used to improve MRS performance (for instance in (Schedl and Bauer, 2017; Bauer and Schedl, 2018b; Schedl and Bauer, 2017)).
To a considerable extent, ideas on the (contextual) components that could improve MRS are based on literature from various disciplines such as cognitive science (e.g., (Stevens, 2012)), social psychology (e.g., (Bonneville-Roussy et al., 2013)), and computer science (Laplante, 2014). In addition, ideas emanate from my own experience of many years in the music domain—which is a significant knowledge source that is not available to every researcher.
Overall, recommender systems research has predominantly focused on improving the prediction accuracy of algorithms based on existing datasets (reflecting users’ historic item ratings or consumption behavior) (Beel et al., 2013). However, to date, comprehensive contextual information about users and the specific situational settings in which those consume the items is rarely available in existing datasets (Adomavicius and Tuzhilin, 2015)—and is especially true for music-related datasets.
The confluence of theoretical and algorithmic modeling seems to be an effective avenue to take in computational modeling for MRS.
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- On the importance of considering country-specific aspects on the online-market: an example of music recommendation considering country-specific mainstream. Conference Proceedings In 51st Hawaii International Conference on System Sciences, HICSS‘18, pp. 3647–3656. External Links: Cited by: §2, §4.
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Improving music recommendation in session-based collaborative filtering by using temporal context.
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Understanding black-box predictions via influence functions.
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- Online music listening culture of kids and adolescents: listening analysis and music recommendation tailored to the young. Conference Proceedings In 1st International Workshop on Children and Recommender Systems, KidRec‘17, New York, NY. Cited by: §4.
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