scholarly journals Characterisation of explicit feedback in an online music recommendation service

2010 ◽  
Author(s):  
Gawesh Jawaheer ◽  
Martin Szomszor ◽  
Patty Kostkova
Keyword(s):  
Music Recommendation ◽  
Online Music ◽  
Explicit Feedback

Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity

2011 ◽  
pp. 203-221 ◽  
Author(s):  
Zehra Cataltepe ◽  
Berna Altinel
Keyword(s):  
Demographic Data ◽  
Recommendation Systems ◽  
Personal Data ◽  
Music Recommendation ◽  
Music Preferences ◽  
User Models ◽  
Online Music ◽  
Important Field ◽  
Case Based

As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative, content-based and case-based recommendation systems and their hybrids have been used for music recommendation. There are already a number of online music recommendation systems. Although specific user information, such as, demographic data, education, and origin have been shown to affect music preferences, they are usually not collected by the online music recommendation systems, because users would not like to disclose their personal data. Therefore, user models mostly contain information about which music pieces a user liked and which ones s/he did not and when.

Online music recommendation platforms as representations of ontologies of musical taste

2018 ◽  
Vol 43 (2) ◽  
pp. 259-281
Author(s):  
Benjamin Krämer
Keyword(s):  
Social Structure ◽  
Social Ontology ◽  
Human Interaction ◽  
The Internet ◽  
Music Recommendation ◽  
Social Proximity ◽  
The Social ◽  
Musical Taste ◽  
Online Music ◽  
Technical Systems

Abstract The framework of the ‘social ontology of the internet’ is applied to music recommendation platforms. Those websites provide individual suggestions of music to users, creating new dynamics of taste that are no longer based on human-to-human interaction and verbalized judgments. An exemplary analysis of three platforms shows that different conceptions of musical tastes are represented by technical systems: situational emotional preferences, a formalist aesthetics, and social proximity based on tastes. The platforms share certain assumptions about the ontology of musical entities and of course the constitutive act of recommending. We discuss how this act can be ascribed to technical systems. Theses on the platforms’ effects on the social structure of musical tastes are developed.

Music Recommendation with Collaborative Filtering for Mobile Services

2014 ◽  
Vol 519-520 ◽  
pp. 510-515 ◽  
Author(s):  
Ya Fei Tang ◽  
Yun Yong Zhang ◽  
Jin Wu Wei ◽  
Xiao Ming Chen
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Matrix Completion ◽  
Mobile Services ◽  
Context Information ◽  
Music Recommendation ◽  
Computational Capability ◽  
The Matrix ◽  
Online Music ◽  
Offline Processing

As the development of the mobile communication and the computational capability of the mobile terminals, more users use their mobile devices to play music. In this work, an online music recommendation system is designed for mobile services, which consists of two modules: offline processing and online recommendation. The offline module labels all the music into different categories, by which the music items libraries corresponding to the tags are constructed and the rating matrixs are consequently built. The online module integrates the context information, by which the matched rating matrix is retrieved. By using the collaborative filtering model with matrix completion algorithm, the music recommendations that suit the user and the situation are offered. The proposed recommendation system improves the precision of the recommendation by integration the context information of the users, and augments the online computational capability because the matrix scale is reduced by constructing the rating matrices for the music in the different tag libraries. A large number of experiments demonstrate that the proposed system is designed to be robust and effective to the music recommendation and efficient to the online recommendation for the mobile services.

scholarly journals A Personalized Music Recommender Based on Potential Preference Learning Dynamically

2011 ◽  
Vol 1 ◽  
pp. 395-399
Author(s):  
Xiao Wei Shi ◽  
Lin Ping Huang ◽  
Wei Jian Mi ◽  
Dao Fang Chang ◽  
Yan Zhang
Keyword(s):  
Recommendation System ◽  
User Profile ◽  
Learning Rate ◽  
The Internet ◽  
Preference Learning ◽  
Music Recommendation ◽  
Potential Interest ◽  
Learning Agent ◽  
Feature Weight ◽  
Explicit Feedback

An intelligent musical recommendation system for multi-users in network context is presented. The system is based on a comprehensive user profile described by feature-weight-like_degree-scene vectors. According different scenes, the system can filter the music that user may like in the internet, and form a music recommendation list which will be sent to the user. The Preference Learning Agent updates the users’ profile dynamically based on explicit feedback or the hidden preference obtained from the users’ behavior. The learning rate of like_degree, original like_degree and the weight of feature type are important for the improvement of the feature’s learning efficiency. The recommendation system can capture the users’ potential interest and the evolvement of preferences. Experiment results show that the algorithm can learn users’ preferences effectively.

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