3,010 Hits in 5.2 sec

Learning to embed music and metadata for context-aware music recommendation

Dongjing Wang, Shuiguang Deng, Xin Zhang, Guandong Xu
2017 World wide web (Bussum)  
In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users' contextual preferences for music.  ...  Then it infers and models users' global and contextual preferences for music from their listening records with the learned embeddings.  ...  components: music embedding model and context-aware music recommendation.  ... 
doi:10.1007/s11280-017-0521-6 fatcat:mqosqqycbbcsvgiupw3rzcysse

CAME: Content- and Context-Aware Music Embedding for Recommendation

Dongjing Wang, Xin Zhang, Dongjin Yu, Guandong Xu, Shuiguang Deng
2020 IEEE Transactions on Neural Networks and Learning Systems  
Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content- and context-aware music recommendation method.  ...  Then, a novel method called content- and context-aware music embedding (CAME) is proposed to obtain the low-dimension dense real-valued feature representations (embeddings) of music pieces from HIN.  ...  Content-and Context-Aware Music Recommendation With the learned embeddings, users' general and contextual preferences can be inferred from their historical music listening records [13] , [50] .  ... 
doi:10.1109/tnnls.2020.2984665 pmid:32305946 fatcat:ody4ay2swfepvhmofyvgv36n6q

Learning Continuous User Representations through Hybrid Filtering with doc2vec [article]

Simon Stiebellehner, Jun Wang, Shuai Yuan
2017 arXiv   pre-print
Second, we introduce context awareness to that model by incorporating additional user and app-related metadata in model training (context2vec).  ...  using doc2vec prove to be highly valuable features in supervised machine learning models for look-alike modeling.  ...  Therefore, we investigate establishing context awareness in hybrid filtering using doc2vec by incorporating supplementary metadata with the aim of improving the vector representations of users.  ... 
arXiv:1801.00215v1 fatcat:avcszeivnjbjxkem6nfe4ctjci

A Mobile-Based System for Context-Aware Music Recommendations [chapter]

Börje F. Karlsson, Karla Okada, Tomaz Noleto
2012 IFIP Advances in Information and Communication Technology  
We present a system for collecting context and usage data from mobile devices, but targeted at recommending music according to learned user profiles and specific situations.  ...  As mobile devices are always with users and music listening is a very personal and situational behaviour, contextual information could be used to greatly enhance music recommendations.  ...  We would also like to thank Ingemar Larsson for being the tireless champion of this idea, and for making sure everything ran as smoothly as possible.  ... 
doi:10.1007/978-3-642-33412-2_53 fatcat:5hvzkrzwdrayhg6ayf6q5ezamq

Context-Aware Media Recommendations

Abayomi Moradeyo Otebolaku, Maria Teresa Andrade
2014 2014 28th International Conference on Advanced Information Networking and Applications Workshops  
Context-aware media recommendation systems take context information such as user preferences, activities, time, location, device, and network capabilities as inputs for media recommendations, whereas the  ...  Media content recommendations for a mobile user based on his changing contextual preferences, otherwise called context-aware media recommendations, constitute a very important challenge.  ...  ACKNOWLEDGMENT The work presented in this paper was partly supported by: Portuguese Foundation for Science and Technology within project FCT/UTA-Est/MAI/0010/2009; the North Portugal Regional Operational  ... 
doi:10.1109/waina.2014.40 dblp:conf/aina/OtebolakuA14 fatcat:5wz77rqmi5e6boexskoqvgbwwi

Trends in content-based recommendation

Pasquale Lops, Dietmar Jannach, Cataldo Musto, Toine Bogers, Marijn Koolen
2019 User modeling and user-adapted interaction  
Meta-Prod2Vec (Vasile et al. 2016) , for instance, is an approach that computes low-dimensional embeddings of item metadata for sequence-based item recommendation in a hybrid model that uses music playlists  ...  relies on embeddings to encode metadata.  ... 
doi:10.1007/s11257-019-09231-w fatcat:ftunw4mq5vgojifno3yqklfwbq

Content-based Music Recommendation: Evolution, State of the Art, and Challenges [article]

Yashar Deldjoo, Markus Schedl, Peter Knees
2021 arXiv   pre-print
context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start.  ...  The music domain is among the most important ones for adopting recommender systems technology.  ...  Kaminskas and Ricci [2012] provide a survey on context-aware music recommendation and retrieval.  ... 
arXiv:2107.11803v1 fatcat:4hz4hqkkmvcapbdr3wvtp2t4iu

Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading [article]

Christian Hansen
2021 arXiv   pre-print
This thesis makes methodological contributions and new investigations of sequential modelling for the specific application areas of systems that recommend music tracks to listeners and systems that process  ...  One example is systems that interact with users, log user actions and behaviour, and make recommendations of items of potential interest to users on the basis of their previous interactions.  ...  While generic context-aware recommender systems have been studied in the past [28] , little work has been focused on music recommendation. We aim to address this gap.  ... 
arXiv:2109.06736v1 fatcat:xawmkvzhgng3vhhrs5xvwokqna

A Systematic Review on Context-Aware Recommender Systems using Deep Learning and Embeddings [article]

Igor André Pegoraro Santana, Marcos Aurelio Domingues
2020 arXiv   pre-print
A systematic review was conducted to understand how the Deep Learning and Embeddings techniques are being applied to improve Context-Aware Recommender Systems.  ...  Context-Aware Recommender Systems were created, accomplishing state-of-the-art results and improving traditional recommender systems.  ...  Acknowledgements The authors would like to thanks CAPES/CNPq for financial support.  ... 
arXiv:2007.04782v1 fatcat:7s6k6ixewzevbabk7s4377dvxy

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste

Zahra Nazari, Christophe Charbuillet, Johan Pages, Martin Laurent, Denis Charrier, Briana Vecchione, Ben Carterette
2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  
Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge.  ...  We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.  ...  : This model combines the user embedding with the metadata categories, but does not include demographic features. (9) Demographics + all music data: This model combines the user embedding, metadata, and  ... 
doi:10.1145/3397271.3401101 dblp:conf/sigir/NazariCPLCVC20 fatcat:uwiovcywbzdzfjjy5abo5dlzz4

Deep Learning in Music Recommendation Systems

Markus Schedl
2019 Frontiers in Applied Mathematics and Statistics  
Deep neural networks are used in this domain particularly for extracting latent factors of music items from audio signals or metadata and for learning sequential patterns of music items (tracks or artists  ...  This review article explains particularities of the music domain in RS research. It gives an overview of the state of the art that employs deep learning for music recommendation.  ...  Integrating additional contextual information, such as user's activity or apps interacted with while listening to music on smart devices, into context-aware MRS that nowadays are commonly powered by DL  ... 
doi:10.3389/fams.2019.00044 fatcat:vvosxlygrravloyrj3qubflnoe

PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

Tiancheng Shen, Jia Jia, Yan Li, Yihui Ma, Yaohua Bu, Hanjie Wang, Bo Chen, Tat-Seng Chua, Wendy Hall
With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music.  ...  For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback  ...  It models users' global and contextual music preferences from their listening records with music embeddings. • DCUE (Lee et al. 2018 ). Short for Deep Content-User Embedding model.  ... 
doi:10.1609/aaai.v34i01.5352 fatcat:jh5uvn5ynfbyzadjffdafjwipe

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Shu Wang, Chonghuan Xu, Austin Shijun Ding, Zhongyun Tang
2021 Electronics  
Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions.  ...  Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations.  ...  Other improvements in emotion-aware music recommendations including the context-based method [24] , hybrid approaches [25] , incorporation of deep learning methods [26] , etc.  ... 
doi:10.3390/electronics10151769 fatcat:ej2zxbjwb5c2popykdv3ezip6u

User-Aware Music Retrieval

Markus Schedl, Sebastian Stober, Emilia Gómez, Nicola Orio, Cynthia C.S. Liem, Marc Herbstritt
2012 Dagstuhl Publications  
We then propose and discuss various requirements for a personalized, user-aware music retrieval system.  ...  A context-aware system, in contrast, takes into account dynamic aspects of the user context when processing the data (e.g., location and time where/when a user issues a query).  ...  User-Aware Music Recommendation Baltrunas et al. present a user-aware music recommender system for usage in cars [7] . They aim at learning relations between user aspects and music genres.  ... 
doi:10.4230/dfu.vol3.11041.135 dblp:conf/dagstuhl/SchedlSGOL12 fatcat:weo2yddzzrfwjkgmfk7zrs3apy

News Session-Based Recommendations using Deep Neural Networks

Gabriel de Souza Pereira Moreira, Felipe Ferreira, Adilson Marques da Cunha
2018 Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems - DLRS 2018  
Some promising results have been recently achieved by the usage of Deep Learning techniques on Recommender Systems, specially for item's feature extraction and for session-based recommendations with Recurrent  ...  In this paper, it is proposed an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News Recommender Systems.  ...  ACKNOWLEDGMENTS The authors would like to thank for providing context on its challenges for large-scale news recommender systems and for sharing a dataset to make those experiments possible.  ... 
doi:10.1145/3270323.3270328 dblp:conf/recsys/MoreiraFC18 fatcat:rurrhe35b5dhjl7xiu2j4cdg2i
« Previous Showing results 1 — 15 out of 3,010 results