A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Filters
[Paper] Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features
2022
ITE Transactions on Media Technology and Applications
Preserved Fulltext
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
2022
Information
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2022
Electronics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2022
ACM Computing Surveys
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2016
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2016
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2022
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2019
International Journal of Machine Learning and Networked Collaborative Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2022 pre-print arXiv:2011.02260v3 fatcat:qe2oh6g22nalvkgrxqb2bgqvvyOther Versions
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2021
PLoS ONE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2022
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 55,295 results