An Efficient Context-Aware Music Recommendation Based on Emotion and Time Context

2018 ◽  
pp. 215-228
Author(s):  
C. Selvi ◽  
E. Sivasankar
Keyword(s):  
Context Aware ◽  
Music Recommendation

A Context-Aware Music Recommendation Agent in Smart Office

2006 ◽  
pp. 1201-1204 ◽  
Author(s):  
Donghai Guan ◽  
Qing Li ◽  
Sungyoung Lee ◽  
Youngkoo Lee
Keyword(s):  
Context Aware ◽  
Music Recommendation

scholarly journals Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes

2021 ◽  
Vol 3 ◽  
Author(s):  
Markus Schedl ◽  
Christine Bauer ◽  
Wolfgang Reisinger ◽  
Dominik Kowald ◽  
Elisabeth Lex
Keyword(s):  
Recommendation System ◽  
State Of The Art ◽  
Music Listening ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Preferences ◽  
User Models ◽  
Gating Mechanism ◽  
Country Clusters ◽  
Country Specific

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the “music mainstream” strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user’s country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.

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.

A Personalized and Context Aware Music Recommendation System

2020 ◽  
pp. 616-627
Author(s):  
Champika H. P. D. Wishwanath ◽  
Supuni N. Weerasinghe ◽  
Kanishka H. Illandara ◽  
A. S. T. M. R. D. S. Kadigamuwa ◽  
Supunmali Ahangama
Keyword(s):  
Recommendation System ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Recommendation System

scholarly journals Sequence-based context-aware music recommendation

2017 ◽  
Vol 21 (2-3) ◽  
pp. 230-252 ◽  
Author(s):  
Dongjing Wang ◽  
Shuiguang Deng ◽  
Guandong Xu
Keyword(s):  
Context Aware ◽  
Music Recommendation

scholarly journals Detection of Music Mood for Context-aware Music Recommendation

2010 ◽  
Vol 17B (4) ◽  
pp. 263-274
Author(s):  
Jong-In Lee ◽  
Dong-Gyu Yeo ◽  
Byeong-Man Kim
Keyword(s):  
Context Aware ◽  
Music Recommendation

Context-Aware Music Recommendation with Serendipity Using Semantic Relations

2014 ◽  
pp. 17-32 ◽  
Author(s):  
Mian Wang ◽  
Takahiro Kawamura ◽  
Yuichi Sei ◽  
Hiroyuki Nakagawa ◽  
Yasuyuki Tahara ◽  
...  
Keyword(s):  
Semantic Relations ◽  
Context Aware ◽  
Music Recommendation
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