scholarly journals [Paper] Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features

2022 ◽  
Vol 10 (1) ◽  
pp. 8-17
Author(s):  
Keigo Sakurai ◽  
Ren Togo ◽  
Takahiro Ogawa ◽  
Miki Haseyama
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Graph ◽  
Music Recommendation ◽  
Acoustic Features

scholarly journals DAPath: Distance-aware knowledge graph reasoning based on deep reinforcement learning

2021 ◽  
Vol 135 ◽  
pp. 1-12
Author(s):  
Prayag Tiwari ◽  
Hongyin Zhu ◽  
Hari Mohan Pandey
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Graph

GRL: Knowledge graph completion with GAN-based reinforcement learning

2020 ◽  
Vol 209 ◽  
pp. 106421
Author(s):  
Qi Wang ◽  
Yuede Ji ◽  
Yongsheng Hao ◽  
Jie Cao
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Graph

scholarly journals Recommendation Algorithm Based on Knowledge Graph to Propagate User Preference

2021 ◽  
Author(s):  
Zhisheng Yang ◽  
Jinyong Cheng
Keyword(s):  
Deep Learning ◽  
Auxiliary Information ◽  
Substantial Improvement ◽  
User Preference ◽  
Knowledge Graph ◽  
Historical Records ◽  
Music Recommendation ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Recommendation Algorithms

Abstract In recommendation algorithms, data sparsity and cold start problems are always inevitable. In order to solve such problems, researchers apply auxiliary information to recommendation algorithms to mine and obtain more potential information through users' historical records and then improve recommendation performance. This paper proposes a model ST_RippleNet, which combines knowledge graph with deep learning. In this model, users' potential interests are mined in the knowledge graph to stimulate the propagation of users' preferences on the set of knowledge entities. In the propagation of preferences, we adopt a triple-based multi-layer attention mechanism, and the distribution of users' preferences for candidate items formed by users' historical click information is used to predict the final click probability. In ST_RippleNet model, music data set is added to the original movie and book data set, and the improved loss function is applied to the model, which is optimized by RMSProp optimizer. Finally, tanh function is added to predict click probability to improve recommendation performance. Compared with the current mainstream recommendation methods, ST_RippleNet recommendation algorithm has very good performance in AUC and ACC, and has substantial improvement in movie, book and music recommendation.

Music recommendation based on acoustic features from the recording studio

2020 ◽  
Vol 148 (4) ◽  
pp. 2701-2701
Author(s):  
Tim Ziemer ◽  
Pattararat Kiattipadungkul ◽  
Tanyarin Karuchit
Keyword(s):  
Music Recommendation ◽  
Acoustic Features ◽  
Recording Studio
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