Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

Online music services are increasing in popularity. They enable us to analyze people's music listening behavior based on play logs. Although it is known that people listen to music based on topic (e.g., rock or jazz), we assume that when a user is addicted to an artist, s/he chooses the artist's songs regardless of topic. Based on this assumption, in this paper, we propose a probabilistic model to analyze people's music listening behavior. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling music listening behavior by taking into account the influence of addiction to artists. Second, by using real-world datasets of play logs, we showed the effectiveness of our proposed model. Third, we carried out qualitative experiments and showed that taking addiction into account enables us to analyze music listening behavior from a new viewpoint in terms of how people listen to music according to the time of day, how an artist's songs are listened to by people, etc. We also discuss the possibility of applying the analysis results to applications such as artist similarity computation and song recommendation.Comment: Accepted by The 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017

Personalized Music Recommendation Based on Style Type

As Internet industry constantly develops and the computer penetration rate continues to grow, the number of online music platforms and music users has been able to increase year by year. With that comes more music choices, information overload has become a very prominent problem. Therefore, how to make users choose their favorite music more conveniently is one of the most challenging problems faced by online music recommendation systems. This paper bases on the existing recommendation system research and uses the collaborative filtering algorithm, proposes a music recommendation method from three perspectives: user attributes, music types and time migration. It is found that the online music recommendation from these three perspectives has a good effect, which can provide a reference for the construction of the current online music recommendation system and is also helpful to platform management practice

A Content-Aware Interactive Explorer of Digital Music Collections: The Phonos Music Explorer

La tesi si propone di utilizzare le più recenti tecnologie del Music Information Retrieval (MIR) al fine di creare un esploratore interattivo di cataloghi musicali. Il software utilizza tecniche avanzate quali riduzione di dimensionalità  mediante FastMap, generazione e streaming over-the-network di contenuto audio, segmentazione e estrazione di descrittori da segnali audio. Inoltre, il software è in grado di adattare in real-time il proprio output sulla base di interazioni dell'utent

Explorative Visual Analysis of Rap Music

Detecting references and similarities in music lyrics can be a difficult task. Crowdsourced knowledge platforms such as Genius. can help in this process through user-annotated information about the artist and the song but fail to include visualizations to help users find similarities and structures on a higher and more abstract level. We propose a prototype to compute similarities between rap artists based on word embedding of their lyrics crawled from Genius. Furthermore, the artists and their lyrics can be analyzed using an explorative visualization system applying multiple visualization methods to support domain-specific tasks

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

Mining microblogs for culture-awareness in web adaptation

Prior studies in sociology and human-computer interaction indicate that persons from different countries and cultural origins tend to have their preferences in real-life communication and the usage of web and social media applications. With Twitter data, statistical and machine learning tools, this study advances our understand ing of microblogging in respect of cultural differences and demonstrates possible solutions of inferring and exploiting cultural origins for building adaptive web ap plications. Our findings reveal statistically significant differences in Twitter feature usage in respect of geographic locations of users. These differences in microblogger behaviour and user language defined in user profiles enabled us to infer user country origins with an accuracy of more than 90%. Other user origin predictive solutions we proposed do not require other data sources and human involvement for training the models, enabling the high accuracy of user country inference when exploiting information extracted from a user followers’ network, or with data derived from Twitter profiles. With origin predictive models, we analysed communication and privacy preferences and built a culture-aware recommender system. Our analysis of friend responses shows that Twitter users tend to communicate mostly within their cultural regions. Usage of privacy settings showed that privacy perceptions differ across cultures. Finally, we created and evaluated movie recommendation strategies considering user cultural groups, and addressed a cold-start scenario with a new user. We believe that the findings discussed give insights into the sociological and web research, in particular on cultural differences in online communication

Semantic Approach to Model Diversity in a Social Cloud

Understanding diversity is important in our inclusive society to hedge against ignorance and accommodate plural perspectives. Diversity nowadays can be observed in online social spaces. People from different backgrounds (e.g. gender, age, culture, expertise) are interacting every day around online digital objects (e.g. videos, images and web articles) leaving their social content in different format, commonly as textual comments and profiles. The social clouds around digital objects (i.e. user comments, user profiles and other metadata of digital objects) offer rich source of information about the users and their perspectives on different domains. Although, researchers from disparate disciplines have been working on understanding and measuring diversity from different perspectives, little has been done to automatically measure diversity in social clouds. This is the main objective of this research. This research proposes a semantic driven computational model to systematically represent and automatically measure diversity in a social cloud. Definitions from a prominent diversity framework and Semantic Web techniques underpin the proposed model. Diversity is measured based on four diversity indices - variety, balance, coverage and (within and across) disparity with regards to two perspectives – (a) domain, which is captured in user comments and represented by domain ontologies, and (b) user, which is captured in profiles of users who made the comments and represented by a proposed User Diversity Ontology. The proposed model is operationalised resulting in a Semantic Driven Diversity Analytics Tool (SeDDAT), which is responsible for diversity profiling based on the diversity indices. The proposed approach of applying the model is illustrated on social clouds from two social spaces - open (YouTube) and closed (Active Video Watching (AVW-Space)). The open social cloud shows the applicability of the model to generate diversity profiles of a large pool of videos (600) with thousands of users and comments. Closed social clouds of two user groups around same set of videos illustrate transferability and further utility of the model. A list of possible diversity patterns within social clouds is provided, which in turn deepen the understanding of diversity and open doors for further utilities of the diversity profiles. The proposed model is applicable in similar scenarios, such as in the social clouds around MOOCs and news articles