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

Keigo Sakurai, Ren Togo, Takahiro Ogawa, Miki Haseyama
2022 ITE Transactions on Media Technology and Applications  
Conventional recommendation methods based on knowledge graphs have struggled with the coldstart problem caused by a lack of user preference information.  ...  The proposed method can make appropriate recommendations even with a small amount of user preference information by learning the optimal action of the agent.  ...  An agent can explore nodes on the graph by regarding the knowledge graph as an environment for reinforcement learning.  ... 
doi:10.3169/mta.10.8 fatcat:6lglumewgrex7pmvgblfdlbtcu

A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation

Xi Liu, Rui Song, Yuhang Wang, Hao Xu
2022 Information  
Knowledge graph (KG) helps to improve the accuracy, diversity, and interpretability of a recommender systems.  ...  KG has been applied in recommendation systems, exploiting graph neural networks (GNNs), but most existing recommendation models based on GNNs ignore the influence of node types and the loss of information  ...  Acknowledgments: The authors would like to thank all of anonymous reviewers and editors for their helpful suggestions for the improvement of this paper.  ... 
doi:10.3390/info13050229 fatcat:k4fqottd3vaoncy5r7kgdty4zi

Enhancing Knowledge of Propagation-Perception-Based Attention Recommender Systems

Hanzhong Zhang, Yinglong Wang, Chao Chen, Ruixia Liu, Shuwang Zhou, Tianlei Gao
2022 Electronics  
It identifies the influence of propagation neighbors on user preferences through a more precise representation of the preference semantics for head and tail entities.  ...  Researchers have introduced side information such as social networks or knowledge graphs to alleviate the problems of data sparsity and cold starts in recommendation systems.  ...  Electronics 2022, 11, x FOR PEER REVIEW 6 of 20 knowledge graph. The second part is the asymmetric attention mechanism layer, which is used for high-quality learning of user preferences in triples.  ... 
doi:10.3390/electronics11040547 fatcat:wnqjgwbduvcxpb4bw6pc3pkdv4

Graph Neural Networks in Recommender Systems: A Survey

Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui
2022 ACM Computing Surveys  
In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any).  ...  Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority  ...  For the works [35, 134, 154] that regard the user nodes as one type of entities, the users' preferences are expected to be spilled over to the entities in the knowledge graph during the propagation process  ... 
doi:10.1145/3535101 fatcat:hgv2tbx3k5hzbnkupwsysqwjmy

SOCIAL METRICS APPLIED TO SMART TOURISM

O. Cervantes, E. Gutiérrez, F. Gutiérrez, J. A. Sánchez
2016 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations.  ...  We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general) to discover cultural heritage  ...  Modeling social and semantic networks using graphs has opened opportunities for exploring alternatives for implementing recommender systems.  ... 
doi:10.5194/isprs-annals-iv-4-w1-117-2016 fatcat:lm2x7iadhvca3bw3rjrfqhfdl4

SOCIAL METRICS APPLIED TO SMART TOURISM

O. Cervantes, E. Gutiérrez, F. Gutiérrez, J. A. Sánchez
2016 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
We describe the semantic network built on graph model, as well as social metrics algorithms used to produce recommendations.  ...  We present a strategy to make productive use of semantically-related social data, from a user-centered semantic network, in order to help users (tourists and citizens in general) to discover cultural heritage  ...  Modeling social and semantic networks using graphs has opened opportunities for exploring alternatives for implementing recommender systems.  ... 
doi:10.5194/isprs-annals-iii-4-w1-117-2016 fatcat:a3ihp3n7s5d75put4fioprkmsm

Knowledge-Aware Multispace Embedding Learning for Personalized Recommendation

Meng Jian, Chenlin Zhang, Xin Fu, Lifang Wu, Zhangquan Wang
2022 Sensors  
In this work, we explore the semantic correlations between items on modeling users' interests and propose knowledge-aware multispace embedding learning (KMEL) for personalized recommendation.  ...  High-order semantic collaborative signals are extracted in multiple independent semantic spaces and aggregated to describe users' interests in each specific semantic.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22062212 pmid:35336383 pmcid:PMC8954710 fatcat:zsuwtkqmwbc5zgm7ijvhbrkjk4

Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

Sajisha P. S, Anoop V.S, Ansal K. A
2019 International Journal of Machine Learning and Networked Collaborative Engineering  
Another recent approach that explored the higher order user preference on KGs for recommendation engines [12] was reported in the recommender system literature by Hogwei Wang et. al.  ...  The system was developed keeping an aim of incorporating preferences of the users on knowledge graphs for recommendation systems.  ... 
doi:10.30991/ijmlnce.2019v03i03.004 fatcat:45ornhc7qzceffqffv7z4xfdd4

MRP2Rec: Exploring Multiple-step Relation Path Semantics for Knowledge Graph-Based Recommendations

Ting Wang, Daqian Shi, Zhaodan Wang, Shuai Xu, Hao Xu
2020 IEEE Access  
approach that learns user preferences for recommendations through preference propagation on the knowledge graph.  ...  INTRODUCTION Recommender systems (RS) have become increasingly important for presenting information to users that meets their personalized preferences.  ... 
doi:10.1109/access.2020.3011279 fatcat:tbztgj6qljgsnanmmpubvufcte

Graph Neural Networks in Recommender Systems: A Survey [article]

Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui
2022 arXiv   pre-print
In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any).  ...  Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority  ...  For the works [35, 133, 153] that regard the user nodes as one type of entities, the users' preferences are expected to be spilled over to the entities in the knowledge graph during the propagation process  ... 
arXiv:2011.02260v4 fatcat:hvk22yyid5bzjnzmzchyti25ja

Recent Advances in Heterogeneous Relation Learning for Recommendation [article]

Chao Huang
2021 arXiv   pre-print
To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation.  ...  In this survey, we review the development of recommendation frameworks with the focus on heterogeneous relational learning, which consists of different types of dependencies among users and items.  ...  Due to the lack of modeling high-order connectivity between users and items, these methods cannot capture the long-range dependencies for user-item interactions and knowledge graphs.  ... 
arXiv:2110.03455v1 fatcat:fskj4qdsibfnxefklazdli3tgu

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation [article]

Yufei Feng, Binbin Hu, Fuyu Lv, Qingwen Liu, Zhiqiang Zhang, Wenwu Ou
2020 arXiv   pre-print
Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications.  ...  Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately.  ...  relational graph for the given target user-item pair over knowledge graph, where the graph connect and graph prune techniques help mine high-order connective structure in an automatic manner; (2) To jointly  ... 
arXiv:2005.12002v1 fatcat:uvjxqnmtdfhchcjhjncifyijge

URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph [article]

Na zhao, Zhen Long, Zhi-Dan Zhao, Jian Wang
2021 arXiv   pre-print
Recently, Knowledge Graph (KG) has been proven to be an effective tool to improve the performance of recommendation systems.  ...  However, a huge challenge in applying knowledge graphs for recommendation is how to use knowledge graphs to obtain better user codes and item codes.  ...  The remainder of this paper is organized as follows: In section 2, we specify the Item Encoding Layer 2.1, the User Encoding Layer 2.2, the Prediction Layer 2.3, the Model Optimization 2.4, and the Experimental  ... 
arXiv:2111.00739v1 fatcat:oxop5gnm2bfqfbga3tkrl5eshm

MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph

Xintao Ma, Liyan Dong, Yuequn Wang, Yongli Li, Hao Zhang, Qi Zhao
2021 PLoS ONE  
And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated.  ...  In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs.  ...  Acknowledgments We would like to thank Hao Zhang (Jilin University) for the insightful comments on the manuscript and his guidance and patience enlighten us not only on this paper but also our future.  ... 
doi:10.1371/journal.pone.0258410 pmid:34710122 pmcid:PMC8553089 fatcat:apyneopnl5dujemai4zihownpq

CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation

Zhuoming Xu, Hanlin Liu, Jian Li, Qianqian Zhang, Yan Tang
2022 Applied Sciences  
relations in the knowledge graph and the high-order connection patterns between entities to provide personalized recommendations.  ...  Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems in recent years.  ...  Data Availability Statement: The datasets are available from the URLs provided in the article. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12031669 fatcat:wi4xwnajr5ekth3fbtafw2p5b4
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