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Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
[article]
2021
arXiv
pre-print
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Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. ...
Compared with existing works that use heavy recurrent neural networks to model temporal information, we propose simple but effective neural networks to capture users' historical item features and path-based ...
For each user, we treat the training items as a session. • NARRE [45] : NARRE utilizes neural attention mechanism to build an explainability recommendation system. • MCRec [5] : MCRec develops a deep ...
arXiv:2111.12262v1
fatcat:rcgzx3akvjamjjvj4zvy6kptle
Reinforcement Learning over Knowledge Graphs for Explainable Dialogue Intent Mining
2020
IEEE Access
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Finally, we consider a wide range of recently proposed knowledge graph-based recommender systems as baselines, mostly based on deep reinforcement learning and our method performs best. ...
We rely on policy-guided reinforcement learning to identify paths in a graph to confirm concrete paths of inference that serve as interpretable explanations. ...
Our novel PGMD approach relies on a reinforcement learning neural network to navigate an query-specific ad hoc knowledge graph in pursuit of relevant query nodes, via our order-aware forward tracking path ...
doi:10.1109/access.2020.2991257
fatcat:wtgscficrzdozp25zy2arysxpi
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
2020
2020 International Joint Conference on Neural Networks (IJCNN)
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It maintains a local knowledge network to guide decision-making process during the training phase and employs the attention mechanism to discover long-term semantics between items. ...
both reinforcement learning and knowledge graphs for the interactive recommendation. ...
Complexity Analysis
Training Strategy Training the actor-critic network requires train two parts of the neural network simultaneously. ...
doi:10.1109/ijcnn48605.2020.9207010
dblp:conf/ijcnn/Chen0Y00Z20
fatcat:3hlajcrneze4vbsnotyy475roa
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
[article]
2021
arXiv
pre-print
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In this paper, we propose RioGNN, a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture, to navigate complexity of neural network structures ...
form of multi-relational graph representations. ...
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks • 39:21
ACM Transactions on Information Systems, Vol. 1, No. 1, Article 39. Publication date: October 2021. ...
arXiv:2104.07886v2
fatcat:5loufv5m6bcp5ny2rzd7aqnuju
Reinforcement learning on graphs: A survey
[article]
2023
arXiv
pre-print
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Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic ...
In this survey, we provide a comprehensive overview of RL and graph mining methods and generalize these methods to Graph Reinforcement Learning (GRL) as a unified formulation. ...
Gradient Dynamic Electronic Toll Collection Electronic Toll Collection Graph neural network Graph Reinforcement Learning Graph attention network Job Shop Scheduling Problem Long Short-Term Memory MDP ...
arXiv:2204.06127v4
fatcat:xni47mpuijgohfhcpw5mwlqmya
A Comprehensive Survey on Automatic Knowledge Graph Construction
[article]
2023
arXiv
pre-print
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Additionally, we also provide briefs on accessible resources that can help readers to develop practical knowledge graph systems. ...
Automatic knowledge graph construction aims to manufacture structured human knowledge. ...
+ CNN (sentence encoder)/GCN (topological encoder) Pre-defined entity pair graph, pre-trained model, position embeddings AGGCN [171] GCN + Multi-head Attention + DC + FF pre-trained word-vectors, position ...
arXiv:2302.05019v1
fatcat:7in54wjwyzhfnkx755izrkzr3y
IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling
[article]
2023
arXiv
pre-print
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To address this difficulty, a new model is developed in this paper, namely Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling (IMKGA-SM). ...
Then, the knowledge graph link prediction task is modelled as an offline reinforcement learning Markov decision model, which is then abstracted into a unified sequence framework. ...
Mask Mechanism Design In the link prediction task of the recommendation system, due to feature redundancy, lack of sufficient training data and complex model design, the recommendation system is extremely ...
arXiv:2301.02445v4
fatcat:fxxsauabmvdblemu5rw3vgvruq
Personalized News Recommendation: Methods and Challenges
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2106.08934v2 fatcat:mpbs3vr75ncw7npnp5uhwj5v4eOther Versions
We hope this paper can facilitate research on personalized news recommendation as well as related fields in natural language processing and data mining. ...
We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. ...
FedRec [127] learns news representations from news titles via a combination of CNN and multi-head self-attention networks. ...
arXiv:2106.08934v3
fatcat:iagqsw73hrehxaxpvpydvtr26m
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning
[article]
2020
arXiv
pre-print
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Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. ...
In this work, we investigate the potential of leveraging knowledge graph (KG) in dealing with these issues of RL methods for IRS, which provides rich side information for recommendation decision making ...
Inspired by the development of graph neural network [9, 17, 28] , KGAT [34] applies graph attention network [28] framework in a collaborative knowledge graph to learn the user, item and entity embeddings ...
arXiv:2006.10389v1
fatcat:jz7bzqh76zemxfrcuqj23ljdte
Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications
[article]
2022
arXiv
pre-print
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Similarly, graph neural networks (GNN) have also demonstrated their superior performance in supervised learning for graph-structured data. ...
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming. ...
Specifically, it leverages the relational graph module to incorporate static relationships via relational graph provided by prior system knowledge and uses proximity relational graph for dynamic relationships ...
arXiv:2206.07922v2
fatcat:kasomvuuifasdbie32xqui3n7e
Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey
[article]
2022
arXiv
pre-print
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From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. ...
We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. ...
Attention Networks, Transformer, Pointer Net and CopyNet, Deep Reinforcement Learning models, Generative Adversarial Networks (GANs), Knowledge Graph Augmented Neural Networks. ...
arXiv:2105.04387v5
fatcat:yd3gqg45rjgzxbiwfdlcvf3pye
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
[article]
2020
arXiv
pre-print
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This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that ...
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. ...
We summarize various techniques for social-aware recommender systems, including autoencoder, recurrent neural network (RNN), graph neural network (GNN), generative models (GM), and hybrid methods, in ...
arXiv:2004.03774v2
fatcat:q7mehir7hbbzpemw3q5fkby5ty
An Attentive Survey of Attention Models
[article]
2021
arXiv
pre-print
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Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. ...
We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. ...
Recommender Systems Attention has seen a significant usage in recommender systems for user profiling, learning user/item representations, and exploiting auxiliary information such as knowledge graph, social ...
arXiv:1904.02874v3
fatcat:fyqgqn7sxzdy3efib3rrqexs74
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
[article]
2023
arXiv
pre-print
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In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. ...
We first introduce the background and the history of the development of both recommender systems and graph neural networks. ...
graph neural networks in recommender systems is another critical challenge. ...
arXiv:2109.12843v3
fatcat:xj6vnkxnofd75gs5eplifk755i
Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs
2021
Electronics
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meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. ...
To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. ...
adds attention network to traditional item-based collaborative filtering. • Neural Attentive Session-based Recommendation (NASR): NASR is a session-based recommendation algorithm that takes into account ...
doi:10.3390/electronics10141671
fatcat:7w7ihedkpzf7rltkpfagutgose
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