scholarly journals Exploiting Music Play Sequence for Music Recommendation

2017 ◽  
Author(s):  
Zhiyong Cheng ◽  
Jialie Shen ◽  
Lei Zhu ◽  
Mohan Kankanhalli ◽  
Liqiang Nie
Keyword(s):  
Recommender Systems ◽  
Feature Learning ◽  
Music Recommendation ◽  
Streaming Services ◽  
Rich Information ◽  
Music Recommender Systems ◽  
Public Datasets ◽  
Digital Footprints ◽  
Music Streaming ◽  
Music Play

Users leave digital footprints when interacting with various music streaming services. Music play sequence, which contains rich information about personal music preference and song similarity, has been largely ignored in previous music recommender systems. In this paper, we explore the effects of music play sequence on developing effective personalized music recommender systems. Towards the goal, we propose to use word embedding techniques in music play sequences to estimate the similarity between songs. The learned similarity is then embedded into matrix factorization to boost the latent feature learning and discovery. Furthermore, the proposed method only considers the k-nearest songs (e.g., k = 5) in the learning process and thus avoids the increase of time complexity. Experimental results on two public datasets demonstrate that our methods could significantly improve the performance of both rating prediction and top-n recommendation tasks.

‘Don’t mess with my algorithm’: Exploring the relationship between listeners and automated curation and recommendation on music streaming services

First Monday ◽  
2022 ◽  
Author(s):  
Sophie Freeman ◽  
Martin Gibbs ◽  
Bjørn Nansen
Keyword(s):  
Recommender Systems ◽  
Participant Observation ◽  
Music Listening ◽  
Music Recommendation ◽  
Structured Interviews ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
The Individual ◽  
The Relationship ◽  
Music Streaming

Given access to huge online collections of music on streaming platforms such as Spotify or Apple Music, users have become increasingly reliant on algorithmic recommender systems and automated curation and discovery features to find and curate music. Based on participant observation and semi-structured interviews with 15 active users of music streaming services, this article critically examines the user experience of music recommendation and streaming, seeking to understand how listeners interact with and experience these systems, and asking how recommendation and curation features define their use in a new and changing landscape of music consumption and discovery. This paper argues that through daily interactions with algorithmic features and curation, listeners build complex socio-technical relationships with these algorithmic systems, involving human-like factors such as trust, betrayal and intimacy. This article is significant as it positions music recommender systems as active agents in shaping music listening habits and the individual tastes of users.

scholarly journals User models for multi-context-aware music recommendation

2020 ◽  
Author(s):  
Martin Pichl ◽  
Eva Zangerle
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Consumption ◽  
Current Context ◽  
Music Recommender Systems ◽  
Context Features ◽  
Content Information ◽  
Music Streaming

Abstract In the last decade, music consumption has changed dramatically as humans have increasingly started to use music streaming platforms. While such platforms provide access to millions of songs, the sheer volume of choices available renders it hard for users to find songs they like. Consequently, the task of finding music the user likes is often mitigated by music recommender systems, which aim to provide recommendations that match the user’s current context. Particularly in the field of music recommendation, adapting recommendations to the user’s current context is critical as, throughout the day, users listen to different music in numerous different contexts and situations. Therefore, we propose a multi-context-aware user model and track recommender system that jointly exploit information about the current situation and musical preferences of users. Our proposed system clusters users based on their situational context features and similarly, clusters music tracks based on their content features. By conducting a series of offline experiments, we show that by relying on Factorization Machines for the computation of recommendations, the proposed multi-context-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems.

scholarly journals Context-Aware Music Recommender Systems for Groups: A Comparative Study

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 506
Author(s):  
Adrián Valera ◽  
Álvaro Lozano Murciego ◽  
María N. Moreno-García
Keyword(s):  
Comparative Study ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Context Aware ◽  
The Past ◽  
Single User ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Music Streaming ◽  
Group Recommender

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

scholarly journals Support the underground: characteristics of beyond-mainstream music listeners

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Dominik Kowald ◽  
Peter Muellner ◽  
Eva Zangerle ◽  
Christine Bauer ◽  
Markus Schedl ◽  
...  
Keyword(s):  
Popular Music ◽  
Recommender Systems ◽  
Demographic Characteristics ◽  
User Models ◽  
Recommendation Algorithms ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Recommendation Accuracy ◽  
Music Streaming

AbstractMusic recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup’s openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.

scholarly journals Visualization of the Artist Relations Using Twitter User Profiles

2020 ◽  
Author(s):  
Minoru Yoshida ◽  
Shogo Kohno ◽  
Kazuyuki Matsumoto ◽  
Kenji Kita
Keyword(s):  
User Profile ◽  
Word Embedding ◽  
New Music ◽  
User Profiles ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Twitter User ◽  
Streaming Services ◽  
Music Streaming

We propose a new music artist recommendation algorithm using Twitter profile texts. Today, music recommendation is provided in many music streaming services. In this paper, we propose a new recommendation algorithm for this music recommendation task. Our idea is to use Twitter profile texts to find appropriate artist names to recommend. We obtained word embedding vectors for each artist name by applying word2vec algorithm to the corpus obtained by collecting such user profile texts, resulting in vectors that reflect artist co-occurrence in the profile texts.

scholarly journals Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 439 ◽  
Author(s):  
Diego Sánchez-Moreno ◽  
Vivian López Batista ◽  
M. Dolores Muñoz Vicente ◽  
Ángel Luis Sánchez Lázaro ◽  
María N. Moreno-García
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
User Behavior ◽  
Error Rates ◽  
User Preferences ◽  
Music Recommendation ◽  
Power Law Distribution ◽  
Long Tail ◽  
Music Recommender Systems ◽  
Structure Metrics

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates.

scholarly journals Development of a Music Recommender System Based on Content Metadata Processing

2019 ◽  
Vol 17 (3) ◽  
pp. 43-60
Author(s):  
A. V. Menkin
Keyword(s):  
Recommender System ◽  
New Approach ◽  
Different Types ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Sparsity Problem ◽  
Metadata Processing ◽  
Essential Problem ◽  
Music Streaming ◽  
Tiny Part

Music recommender systems (MRS) help users of music streaming services to find interesting music in the music catalogs. The sparsity problem is an essential problem of MRS research. It refers to the fact that user usually rates only a tiny part of items. As a result, MRS often has not enough data to make a recommendation. To solve the sparsity problem, in this paper, a new approach that uses related items’ ratings is proposed. Hybrid MRS based on this approach is described. It uses tracks, albums, artists, genres normalized ratings along with information about relations between items of different types in the music catalog. The proposed MRS is evaluated and compared to collaborative method for users’ preferences prediction.

Market Capture of Music Streaming Services in India

2020 ◽  
pp. 65-67
Keyword(s):  
Country Music ◽  
Internet Services ◽  
Indian Market ◽  
Internet Users ◽  
Streaming Services ◽  
Market Capture ◽  
Streaming Service ◽  
Music Streaming ◽  
Day By Day

India is a very vast market for internet services as it has over 480 million active internet users in the country. Music streaming services in India is emerging day by day. The competition in the market is so high that even two giants Jio Music and Saavn join their hand in 2018 to provide a combine service all across the globe. In, 2019 a global giant Spotify entered into music streaming market in India and affected the each music service in India. Gaana owned by Times Internet have over 150 million active monthly users in the country while JioSaavn reported 100 million active monthly users as per a website. This research is going to study the market capture of various music streaming services in India. Currently, as per the research, Spotify is the most popular streaming service. As per the literature available on various platforms other streaming services were holding the major proportion of the Indian market but after the launch of Spotify, it became most loved streaming service. The research is being done to find out the existing music streaming services are affected by the entrance of Spotify or not

scholarly journals CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

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