Advances in next-track music recommendation

Technological advances in the music industry have dramatically changed how people access and listen to music. Today, online music stores and streaming services offer easy and immediate means to buy or listen to a huge number of songs. One traditional way to find interesting items in such cases when a vast amount of choices are available is to ask others for recommendations. Music providers utilize correspondingly music recommender systems as a software solution to the problem of music overload to provide a better user experience for their customers. At the same time, an enhanced user experience can lead to higher customer retention and higher business value for music providers. Different types of music recommendations can be found on today's music platforms, such as Spotify or Deezer. Providing a list of currently trending music, finding similar tracks to the user's favorite ones, helping users discover new artists, or recommending curated playlists for a certain mood (e.g., romantic) or activity (e.g., driving) are examples of common music recommendation scenarios. "Next-track music recommendation" is a specific form of music recommendation that relies mainly on the user's recently played tracks to create a list of tracks to be played next. Next-track music recommendations are used, for instance, to support users during playlist creation or to provide personalized radio stations. A particular challenge in this context is that the recommended tracks should not only match the general taste of the listener but should also match the characteristics of the most recently played tracks. This thesis by publication focuses on the next-track music recommendation problem and explores some challenges and questions that have not been addressed in previous research. In the first part of this thesis, various next-track music recommendation algorithms as well as approaches to evaluate them from the research literature are reviewed. The recommendation techniques are categorized into the four groups of content-based filtering, collaborative filtering, co-occurrence-based, and sequence-aware algorithms. Moreover, a number of challenges, such as personalizing next-track music recommendations and generating recommendations that are coherent with the user's listening history are discussed. Furthermore, some common approaches in the literature to determine relevant quality criteria for next-track music recommendations and to evaluate the quality of such recommendations are presented. The second part of the thesis contains a selection of the author's publications on next- track music recommendation as follows. 1. The results of comprehensive analyses of the musical characteristics of manually created playlists for music recommendation; 2. the results of a multi-dimensional comparison of different academic and commercial next-track recommending techniques; 3. the results of a multi-faceted comparison of different session-based recommenders, among others, for the next-track music recommendation problem with respect to their accuracy, popularity bias, catalog coverage as well as computational complexity; 4. a two-phase approach to recommend accurate next-track recommendations that also match the characteristics of the most recent listening history; 5. a personalization approach based on multi-dimensional user models that are extracted from the users' long-term preferences; 6. a user study with the aim of determining the quality perception of next-track music recommendations generated by different algorithms

How Automated Recommendations Affect the Playlist Creation Behavior of Users

International audienceModern music platforms like Spotify support users to create new playlists through interactive tools. Given an empty or initial playlist, these tools often recommend additional songs, which could be included in the playlist based, e.g., on the title of the playlist or the set of tracks that are already in the playlist. In this work, we analyze in which ways the recommendations of such playlist construction support tools influence the behavior of users and the characteristics of the resulting playlists. We report the results of a between-subjects user study involving 123 subjects. Our analysis shows that users provided with recommendation support were more engaged and explored more alternatives than the control group. Presumably influenced by the recommender, they also picked significantly less popular items, which leads to a higher potential for discovery. The effort required to browse the additional alternatives, however, increased the users' perceived difficulty of the process

Effects of recommendations on the playlist creation behavior of users

International audienceThe digitization of music, the emergence of online streaming platforms and mobile apps have dramatically changed the ways we consume music. Today, much of the music that we listen to is organized in some form of a playlist, and many users of modern music platforms create playlists for themselves or to share them with others. The manual creation of such playlists can however be demanding, in particular due to the huge amount of possible tracks that are available online. To help users in this task, music platforms like Spotify provide users with interactive tools for playlist creation. These tools usually recommend additional songs to include given a playlist title or some initial tracks. Interestingly, little is known so far about the effects of providing such a recommendation functionality. We therefore conducted a user study involving 270 subjects, where one half of the participants-the treatment group-were provided with automated recommendations when performing a playlist construction task. We then analyzed to what extent such recommendations are adopted by users and how they influence their choices. Our results, among other aspects, show that about two thirds of the treatment group made active use of the recommendations. Further analyses provide additional insights about the underlying reasons why users selected certain recommendations. Finally, our study also reveals that the mere presence of the recommendations impacts the choices of the participants, even in cases when none of the recommendations was actually chosen

Music Recommendations

International audienceToday's online music services like Spotify provide their listeners with different types of music recommendations, e.g., in the form of weekly recommendations or personalized radio stations. Such recommendations are often based, at least in parts, on collaborative filtering techniques. In this chapter, we first review the different types of music recommendations that can be found in practice and discuss the specific challenges of the domain. Next, we discuss technical approaches for the problems of music discovery and next-track recommendation in more depth, with a specific focus on their practical application at Spotify. Finally, we further elaborate on open challenges in the field and revisit the specific problems of evaluating music recommendation systems in academic environments

Personalized Next-Track Music Recommendation with Multi-dimensional Long-Term Preference Signals

ABSTRACT The automated generation of playlists given a user's most recent listening history is a common feature of modern music streaming platforms. In the research literature, a number of algorithmic proposals for this "next-track recommendation" problem have been made in recent years. However, nearly all of them are based on the user's most recent listening history, context, or location but do not consider the users' long-term listening preferences or social network. In this work, we explore the value of long-term preferences for personalizing the playlist generation process and evaluate different strategies of applying multi-dimensional user-specific preference signals. The results of an empirical evaluation on five different datasets show that although the short-term listening history should generally govern the next-track selection process, long-term preferences can measurably help to increase the personalization quality

Biases in Automated Music Playlist Generation: A Comparison of Next-Track Recommending Techniques

International audiencePlaylist generation is a special form of music recommendation where the problem is to create a sequence of tracks to be played next, given a number of seed tracks. In academia, the evaluation of playlisting techniques is often done by assessing with the help of Information Retrieval measures if an algorithm is capable of selecting those tracks that also a human would pick next. Such approaches however cannot capture other factors, e.g., the homogeneity of the tracks that can determine the quality perception of playlists. In this work, we report the results of a multi-metric comparison of different academic approaches and a commercial playlisting service. Our results show that all tested techniques generate playlists with certain biases, e.g., towards very popular tracks, and often create playlists continuations that are quite different from those that are created by real users