134 research outputs found
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
A collaborative filtering method for music recommendation
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe present dissertation focuses on proposing and describing a collaborative filtering approach for
Music Recommender Systems. Music Recommender Systems, which are part of a broader class of
Recommender Systems, refer to the task of automatically filtering data to predict the songs that are
more likely to match a particular profile.
So far, academic researchers have proposed a variety of machine learning approaches for determining
which tracks to recommend to users. The most sophisticated among them consist, often, on complex
learning techniques which can also require considerable computational resources. However, recent
research studies proved that more simplistic approaches based on nearest neighbors could lead to
good results, often at much lower computational costs, representing a viable alternative solution to
the Music Recommender System problem.
Throughout this thesis, we conduct offline experiments on a freely-available collection of listening
histories from real users, each one containing several different music tracks. We extract a subset of 10
000 songs to assess the performance of the proposed system, comparing it with a Popularity-based
model approach. Furthermore, we provide a conceptual overview of the recommendation problem,
describing the state-of-the-art methods, and presenting its current challenges. Finally, the last section
is dedicated to summarizing the essential conclusions and presenting possible future improvements
Novel music discovery concepts: user experience and design implications
Current music consumers are facing an almost endless selection of music in online services to be accessed on-demand with a variety of devices. The focus has now shifted from providing on-demand access to massive music catalogs towards improving the user experience of the music services, providing new ways of finding relevant music from the massive online catalogs, and making music consumption a pleasurable experience. The key differentiation aspects for music services come largely from the user interface and the ways that music can be found or consumed.
This thesis belongs to the fields of human-computer interation (HCI) and music information retrieval (MIR). HCI is concerned with the design, evaluation and implementation of interactive computing systems and MIR targets to broaden the understanding and usage of musical data through research, applications and tools.
This thesis studies novel concepts for music discovery that are based on strong visual metaphors and stereotypes. The goal is to research the user experience (UX) of novel music discovery services and to formulate key design implications to support service development for music discovery. The research of music discovery prototypes consisted of three main phases: initial concept design phase, playful concept exploration phase, and iterative concept design phase. The thesis introduces, in total, ten prototype implementations of these novel concepts for music discovery. User evaluations of the implemented prototypes were conducted with Finnish active music listeners with both qualitative and quantitative research methods. This thesis contributes to both academic research on HCI in MIR and commercial music discovery service development.
The results provide insights to user experience with different types of novel music discovery services. Five novel music discovery services using the same content-based music recommendation back-end were compared and the comparison results are reported including both first impressions and longer-term usage. Additionally, the results of the studies introduce a wide set of future directions for each music discovery approach. These future directions enable service developers to further enhance the music discovery experience within these fields.
All but one of the proposed music discovery concepts work well for music discovery. The use of avatar characters and mood pictures for music discovery are the most promising ones. The results show that visual music discovery services have the potential to replace traditional music discovery services in different types of music discovery practices. The final contribution of the thesis is a set of 16 design implications for music discovery service developers
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Mood-dynamic playlist : interpolating a path of emotions using a KNN algorithm
Users curate music playlists based on emotion to focus or relax, so streaming services often create playlists of songs to aid this process. Prior research focuses on generating playlists of a single mood or genre, although a few studies work to construct automatic playlists that transition between the genres of their first and last songs. Building upon previous methods, we present a novel method for efficiently generating smooth and even playlists dynamic in both mood and genre. Given starting and target songs and a desired length, our two-stage algorithm sequentially chooses each track in between. It first uses a K-Nearest Neighbor model to retrieve potential songs based on emotional annotations in the Valence-Arousal emotion space, and then a vector distance metric to choose the next song based on audio features. We evaluate the smoothness and evenness of playlists across various regions of an emotional feature space. Our experiments indicate that the quality of track transitions from our algorithm's playlists is dependent on various input parameters and the distribution of the songs in our emotion dataset. This algorithm has potential as a novel music therapy tool, allowing users to create playlists in specific genres that nudge them to a desired mood.Key Words: Music Information Retrieval, Music Emotion Recognition, Automatic Playlist Generation, K-Nearest Neighbo
Algorithmes de recommandation musicale
Ce meĢmoire est composeĢ de trois articles qui sāunissent sous le theĢme de la recommandation musicale aĢ grande eĢchelle.
Nous preĢsentons dāabord une meĢthode pour effectuer des recommandations musicales en reĢcoltant des eĢtiquettes (tags) deĢcrivant les items et en utilisant cette aura textuelle pour deĢterminer leur similariteĢ. En plus dāeffectuer des recommandations qui sont transparentes et personnalisables, notre meĢthode, baseĢe sur le contenu, nāest pas victime des probleĢmes dont souffrent les systeĢmes de filtrage collaboratif, comme le probleĢme du deĢmarrage aĢ froid (cold start problem).
Nous preĢsentons ensuite un algorithme dāapprentissage automatique qui applique des eĢtiquettes aĢ des chansons aĢ partir dāattributs extraits de leur fichier audio. Lāensemble de donneĢes que nous utilisons est construit aĢ partir dāune treĢs grande quantiteĢ de donneĢes sociales provenant du site Last.fm.
Nous preĢsentons finalement un algorithme de geĢneĢration automatique de liste dāeĢcoute personnalisable qui apprend un espace de similariteĢ musical aĢ partir dāattributs audio extraits de chansons joueĢes dans des listes dāeĢcoute de stations de radio commerciale. En plus dāutiliser cet espace de similariteĢ, notre systeĢme prend aussi en compte un nuage dāeĢtiquettes que lāutilisateur est en mesure de manipuler, ce qui lui permet de deĢcrire de manieĢre abstraite la sorte de musique quāil deĢsire eĢcouter.This thesis is composed of three papers which unite under the general theme of large-scale music recommendation.
The first paper presents a recommendation technique that works by collecting text descriptions of items and using this textual aura to compute the similarity between them using techniques drawn from information retrieval. We show how this representation can be used to explain the similarities between items using terms from the textual aura and further how it can be used to steer the recommender. Because our system is content-based, it is not victim of the usual problems associated with collaborative filtering recommenders like the cold start problem.
The second paper presents a machine learning model which automatically applies tags to music. The model uses features extracted from the audio files and was trained on a very large data set constructed with social data from the online community Last.fm.
The third paper presents an approach to generating steerable playlists. We first demonstrate a method for learning song transition probabilities from audio features extracted from songs played in professional radio station playlists. We then show that by using this learnt similarity function as a prior, we are able to generate steerable playlists by choosing the next song to play not simply based on that prior, but on a tag cloud that the user is able to manipulate to express the high-level characteristics of the music he wishes to listen to
Towards a better understanding of music playlist titles and descriptions
Music playlists, either user-generated or curated by music streaming services, often come with titles and descriptions. Although informative, these titles and descriptions make up a sparse and noisy semantic space that is challenging to be leveraged for tasks such as making music recommendations. This dissertation is dedicated to developing a better understanding of playlist titles and descriptions by leveraging track sequences in playlists. Specifically, work has been done to capture latent patterns in tracks by an embedding approach, and the latent patterns are found to be well aligned with the organizing principles of mixtapes identified more than a decade ago. The effectiveness of the latent patterns is evaluated by the task of generating descriptive keywords/tags for playlists given tracks, indicating that the latent patterns learned from tracks in playlists are able to provide a good understanding of playlist titles and descriptions. The identified latent patterns are further leveraged to improve model performance on the task of predicting missing tracks given playlist titles and descriptions. Experimental results show that the proposed models yield improvements to the task, especially when playlist descriptions are provided as model input in addition to titles. The main contributions of this work include (1) providing a better solution to dealing with ``cold-start'' playlists in music recommender systems, and (2) proposing an effective approach to automatically generating descriptive keywords/tags for playlists using track sequences
Visualizing Music Collections Based on Metadata: Concepts, User Studies and Design Implications
Modern digital music services and applications enable easy access to vast online and local music collections. To differentiate from their competitors, software developers should aim to design novel, interesting, entertaining, and easy-to-use user interfaces (UIs) and interaction methods for accessing the music collections. One potential approach is to replace or complement the textual lists with static, dynamic, adaptive, and/or interactive visualizations of selected musical attributes. A well-designed visualization has the potential to make interaction with a service or an application an entertaining and intuitive experience, and it can also improve the usability and efficiency of the system.
This doctoral thesis belongs to the intersection of the fields of human-computer interaction (HCI), music information retrieval (MIR), and information visualization (Infovis). HCI studies the design, implementation and evaluation of interactive computing systems; MIR focuses on the different strategies for helping users seek music or music-related information; and Infovis studies the use of visual representations of abstract data to amplify cognition.
The purpose of the thesis is to explore the feasibility of visualizing music collections based on three types of musical metadata: musical genre, tempo, and the release year of the music. More specifically, the research goal is to study which visual variables and structures are best suitable for representing the metadata, and how the visualizations can be used in the design of novel UIs for music player applications, including music recommendation systems. The research takes a user- centered and constructive design-science approach, and covers all the different aspects of interaction design: understanding the users, the prototype design, and the evaluation.
The performance of the different visualizations from the user perspective was studied in a series of online surveys with 51-104 (mostly Finnish) participants. In addition to tempo and release year, five different visualization methods (colors, icons, fonts, emoticons and avatars) for representing musical genres were investigated. Based on the results, promising ways to represent tempo include the number of objects, shapes with a varying number of corners, and y-axis location combined with some other visual variable or clear labeling. Promising ways to represent the release year include lightness and the perceived location on the z- or x-axis. In the case of genres, the most successful method was the avatars, which used elements from the other methods and required the most screen estate.
In the second part of the thesis, three interactive prototype applications (avatars, potentiometers and a virtual world) focusing on visualizing musical genres were designed and evaluated with 40-41 Finnish participants. While the concepts had great potential for complementing traditional text-based music applications, they were too simple and restricted to replace them in longer-term use. Especially the lack of textual search functionality was seen as a major shortcoming.
Based on the results of the thesis, it is possible to design recognizable, acceptable, entertaining, and easy-to-use (especially genre) visualizations with certain limitations. Important factors include, e.g., the used metadata vocabulary (e.g., set of musical genres) and visual variables/structures; preferred music discovery mode; available screen estate; and the target culture of the visualizations
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