Recommending Music to Groups in Fitness Classes

Proceedings ICMC|SMC|2014 14-20 September 2014, Athens, Greece Recommending Music to Groups in Fitness Classes Berardina De Carolis Stefano Ferilli Nicola Orio University of Bari "Aldo Moro" University of Bari "Aldo Moro" University of Padua [email protected] [email protected] [email protected] ABSTRACT ing the correct music can be the attainment of flow, a state of complete optimal functioning of body and mind Research in the fitness domain proves that music has an on auto-pilot with minimal conscious effort [8]. important motivating effect on the athletes’ performance. Therefore, these considerations suggest that selecting This effect is even stronger when music is used in sport the right music can be crucial for improving performance synchronously like, for instance, in fitness classes. Indoor especially in activities that use music synchronously (i.e. cycling is one of these activities in which music is a key aerobic, step, indoor cycling classes). Research work in issue of success during the lesson, providing a high moti- this domain addresses issues of personalization and tai- vational mean for the instructor towards the classroom. In loring of playlists to single users. In this paper we address this paper we present the result of a study in which we the issue of tailoring the music selection to a group of tested a group recommender system aiming at supporting people in order to motivate the entire class to workout. the instructor music choice when preparing the lesson. To this aim, we looked at several disciplines that could This is done aggregating data present in the individual benefit of this service and we selected indoor cycling. It profiles of each user in the class that are built by combin- is a form of high-intensity exercise that uses a stationary ing explicit and implicit gathering of information about exercise bicycle in a classroom setting. A typical class their music tastes. In order to refine the profiling process, involves a single instructor who leads the participants through the lesson, which is designed to simulate situa- users may express their feedback on the proposed music tions similar to riding a bike outdoors. A well-trained in- tracks after the workout, thus improving the quality of the structor uses music as a motivational means to lead par- future music recommendations. ticipants through a ride that best suits their fitness level and goals. Then, music is a key issue of success during 1. INTRODUCTION the lesson since: i) its rhythm and beats per minute (bpm) The positive effects of music on sport performance and in have an effect on the cadence and the difficulty of pedal- exercise contexts are well-known. Research in the field of ling and ii) it represents a high motivational means for the sport psychology suggests that the effects of music in instructor towards the classroom. stimulating athletic performance has scientific bases [6]. In this paper we present how XMusic, a group recom- Some studies proved that selecting the most appropriate mender system for music, has been applied and tested in music may improve the athlete performance up to 20%. the context of indoor cycling. The system aims at sup- In particular, many research works, which investigate porting the instructor music choice when preparing the the relation between music and sport performance, out- lesson with suggestions about the music tracks to include line that several factors determine the motivational power in the playlist that suits both the preferences of the group of a music track. For instance, factors are related to per- and the motivational goals. ceptual features, such as rhythm and musicality, to the The system is composed of a module for profiling indi- cultural impact or even to the association with a certain vidual members, a group profiling module and a music feeling or a situation (for example “Chariots of Fire by recommender module for creating the playlists. As de- Vangelis is often associated with Olympic glory” [7]). In scribed later in the paper, the group profile is built by ag- addition, there are other personal factors related to the gregating information about music tastes of individual exerciser (gender, age, personality, commitment to exer- users. Individual users’ profiles are built by gathering in- cise, fitness level, etc.) and the context (exercise envi- formation about music preferences both explicitly (ques- ronment and specifics of exercise regimens). There are tionnaires about motivating music tracks) and implicitly different ways in which music aids athletic performance. (mining Facebook profiles). The group modelling strate- According to [6,7], music can distract the mind from sen- gy used by the system is a variation of the average that sations of fatigue (dissociation), music can be used to takes into account the rates of the majority of the group regulate arousal during exercise and a consequence of us- members. However contextual factors such as guests and events (e.g. birthdays) may be taken into account by using the most respected person strategy to give priority to Copyright: © 2014 De Carolis et al. This is an open-access article dis- a particular user. According to the resulting music profile tributed under the terms of the Creative Commons Attribution License 3.0 for the class, the instructor receives recommendations Unported, which permits unrestricted use, distribution, and reproduction about music that is appropriate for a given class. In order in any medium, provided the original author and source are credited. to refine the profiling process, users in the class may express their feedback on the proposed playlist after the - 1759 - Proceedings ICMC|SMC|2014 14-20 September 2014, Athens, Greece workout, thus improving the quality of future music rec- centres to choose music according to the preferences of ommendations. the groups of users present in different rooms. The strate- The paper is organized as follows. Related work is de- gy used by MusicFx is the Average without Misery, scribed in Section 2. In Section 3, we describe the XMu- which is based on the sum of normalized scores of every sic system. An evaluation of our approach is presented in item in the list of preferences. Since this strategy allows Section 4. Conclusions and future research directions are fixing a threshold (a minimum predefined value under discussed in Section 5. which that alternative is cancelled from the final se- quence of interests), it ensures a minimum degree of sat- 2. RELATED WORK isfaction for every item in the final list of music songs. For this reason, the less favourable user can eliminate Group profiling has become increasingly important espe- from the list the pieces he hates, by giving them an evalu- cially in the context of recommender systems in which ation score equal to zero. This, however, may represent a suggestions and recommendations are addressed to a problem since, if several users give a zero score to sever- group of people instead of an individual. These applica- al items, the system will not be able to create a list be- tions may have different purposes as, for instance, cause all the preferences will be equal to zero. Flytrap is a providing information and news on public displays [5] or system that constructs a playlist that tries to please every- a music playlist in an ambient in which a group of people one in an active environment. Users’ musical tastes are is located [10,4]. automatically derived by information about the music The majority of systems that adapt their behaviour to that people listen to on their computers. As in MusicFX groups of users employ two main approaches. The former users are recognized by their active ID badges that let the combines individual recommendations to generate a list system know when they are nearby. The system, using of group recommendations, while the latter computes the preference information it has gathered from watching group recommendations using a group profile derived its users, and knowledge of how music genres interrelate, from individual profiles (e.g. [10,11]). Strategies to ag- how artists have influenced each other, and what kinds of gregate individuals’ preferences are various (see [9,2] for transitions between songs people tend to make, finds a details from the perspective of group recommendation). compromise and chooses a song. Once it has chosen a These strategies try to maximize group satisfaction and/or song, music is automatically broadcast and played. to avoid un-satisfaction of some members in the group or Adaptive Radio is a system that selects music to play in to privilege a particular member. In [12] a novel group a shared environment. Rather than attempting to play the recommendation solution is proposed, which incorporates songs that users want to hear, the system avoids playing both social and content interests of group members. They songs that they do not want to hear. Negative preferences propose a group consensus function that captures the so- can potentially be applied to other domains, such as in- cial, expertise, and interest dissimilarity among group formation ﬁltering, intelligent environments, and collabo- members. rative design. PartyVote is a system that provides estab- In all cases, when developing a group recommender lished groups with a simple democratic mechanism for system, there is a need to know as much as possible of selecting and playing music at social events. Finally, each user for generating the most relevant and appropri- GroupFun [13] is designed to help a group of friends to ate set of recommendations [1]. Sometimes this is not reach a common music playlist starting from their distinct possible and some authors integrate missing information tastes and applying a voting strategy. with a demographic statistical approach [5].

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