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Reinforced Negative Sampling over Knowledge Graph for Recommendation
[article]
2020
pre-print
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Towards this end, we develop a new negative sampling model, Knowledge Graph Policy Network (KGPolicy), which works as a reinforcement learning agent to explore high-quality negatives. ...
In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. ...
Towards this end, we propose a new negative sampling model, KGPolicy (short for Knowledge Graph Policy Network), which employs a reinforcement learning (RL) agent to explore KG to discover high-quality ...
doi:10.1145/3366423.3380098
arXiv:2003.05753v1
fatcat:csmmg25r7jbwxophuvjln6zt6y
Knowledge Graph-enhanced Sampling for Conversational Recommender System
[article]
2021
arXiv
pre-print
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Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient ...
To address the aforementioned issue, this work proposes a contextual information enhancement model tailored for CRS, called Knowledge Graph-enhanced Sampling (KGenSam). ...
item sample nodes in the KG, outputs high-quality negative samples through reinforcement learning, supplements sparse online user data with negative samples, and updates the recommender with positive ...
arXiv:2110.06637v1
fatcat:2e2uez3zynf2dbeeipvnqhtxza
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
[article]
2021
arXiv
pre-print
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of the recent trends of deep reinforcement learning in recommender systems. ...
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview ...
[98] propose a method named Knowledge-guided Reinforcement Learning (KERL), which integrates knowledge graphs into the REINFORCE algorithm. ...
arXiv:2109.03540v2
fatcat:5gwrbfcj3rc7jfkd54eseck5ga
A Survey on Reinforcement Learning for Recommender Systems
[article]
2022
arXiv
pre-print
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In particular, Reinforcement Learning (RL) based recommender systems have become an emerging research topic in recent years. ...
To understand the challenges and relevant solutions, there should be a reference for researchers and practitioners working on RL-based recommender systems. ...
To discover informative negative feedback from the missing data, [92] proposes a KG policy network for knowledge-aware negative sampling, which employs an RL agent to search high-quality negative instances ...
arXiv:2109.10665v2
fatcat:wx5ghn66hzg7faxee54jf7gspq
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
[article]
2021
arXiv
pre-print
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items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. ...
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. ...
INTRODUCTION Reasoning with paths over user-item associated Knowledge Graphs (KGs) has been becoming a popular means for explainable recommendations [1] , [2] , [3] . ...
arXiv:2111.12262v1
fatcat:rcgzx3akvjamjjvj4zvy6kptle
Understanding Negative Sampling in Knowledge Graph Embedding
2021
International Journal of Artificial Intelligence & Applications
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The quality of generated negative samples has a direct impact on the performance of learnt knowledge representation in a myriad of downstream tasks, such as recommendation, link prediction and node classification ...
Most KGs store only positive samples for space efficiency. Negative sampling thus plays a crucial role in encoding triples of a KG. ...
CONCLUSIVE REMARKS In this paper we have reviewed negative sampling in knowledge graph embedding. ...
doi:10.5121/ijaia.2021.12105
fatcat:djli7s2i6ndstjh6zav5mojhdy
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
[article]
2020
arXiv
pre-print
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A promising solution for this problem is to over-sample the "difficult" (a.k.a informative) instances that contribute more on training. ...
Correspondingly, we derive a fast reinforced training algorithm of our framework to boost the sampler performance and sampler-recommender collaboration. ...
[39] leverages knowledge graph to enhance negative sampling. However, these auxiliary information may not be available in many recommendation systems. Random walk in recommendation. ...
arXiv:2011.07739v1
fatcat:wktdowkk7be2dnou63g2pzoslq
[Paper] Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features
2022
ITE Transactions on Media Technology and Applications
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In this study, we propose a new deep reinforcement learning-based music recommendation method with knowledge graphs. ...
Furthermore, we realize efficient search using a deep reinforcement learning algorithm on a dense knowledge graph introducing acoustic feature-based edges. ...
Zhou et al . leveraged a prior knowledge in a knowledge graph to achieve high sample efficiency in a reinforcement learning-based interactive recommendation system [33] . ...
doi:10.3169/mta.10.8
fatcat:6lglumewgrex7pmvgblfdlbtcu
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams
[article]
2022
arXiv
pre-print
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In this paper, we focus on the problem of modeling dynamic geo-human interactions in streams for online POI recommendations. ...
Specifically, we model a mixed-user event stream by unifying all users, visits, and geospatial contexts as a dynamic knowledge graph stream, in order to model human-human, geo-human, geo-geo interactions ...
Knowledge Graph-based Recommendation. Knowledge graph (KG) demonstrates the semantic relations and reasoning structure among different entities. ...
arXiv:2201.10983v1
fatcat:2fysztm4mfba7nugsibghmi5km
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
[article]
2020
arXiv
pre-print
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We propose a knowledge graph (KG)-aware recommender based on graph neural networks, which augments labelled samples through pseudo-labelling. ...
Most present works leverage unobserved samples for extracting negative signals. ...
Sampling Based on Knowledge Graph and Popularity 3.4.1 KG-Aware Item Sampling for Pseudo-Labelling. ...
arXiv:2011.05061v1
fatcat:3xelswj5zrfq5n72ozydj3dxuq
Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation
[article]
2020
arXiv
pre-print
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Moreover, direct reasoning over large-scale knowledge graph can be costly due to the huge search space of pathfinding. ...
from knowledge graph. ...
Figure 1: A coarse-to-fine process of neural-symbolic reasoning over knowledge graph for explainable recommendation. ...
arXiv:2007.13207v1
fatcat:we2und23rvbghoprpjnzbgsxeu
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
[article]
2018
arXiv
pre-print
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Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a non-trivial task. ...
Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator ...
Conclusions We propose a novel adversarial learning method for improving a wide range of knowledge graph embedding models-We designed a generatordiscriminator framework with dual KGE components. ...
arXiv:1711.04071v3
fatcat:3n52ch36evaivh4a2jt5olt65i
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
2018
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
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.
Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a nontrivial task. ...
Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator ...
Conclusions We propose a novel adversarial learning method for improving a wide range of knowledge graph embedding models-We designed a generatordiscriminator framework with dual KGE components. ...
doi:10.18653/v1/n18-1133
dblp:conf/naacl/CaiW18
fatcat:yecuzmprl5eyvmfzvfnsaln5yu
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
2020
2020 International Joint Conference on Neural Networks (IJCNN)
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both reinforcement learning and knowledge graphs for the interactive recommendation. ...
Inspired by the recent advance in Graph Convolutional Network and the knowledge-aware recommendation, we design a Knowledge-Guided deep Reinforcement learning (KGRL) model to harness the advantages of ...
both reinforcement learning and knowledge graphs for the interactive recommendation. ...
doi:10.1109/ijcnn48605.2020.9207010
dblp:conf/ijcnn/Chen0Y00Z20
fatcat:3hlajcrneze4vbsnotyy475roa
Training like Playing: A Reinforcement Learning And Knowledge Graph-based framework for building Automatic Consultation System in Medical Field
[article]
2021
arXiv
pre-print
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We introduce a framework for AI-based medical consultation system with knowledge graph embedding and reinforcement learning components and its implement. ...
Our implement of this framework leverages knowledge organized as a graph to have diagnosis according to evidence collected from patients recurrently and dynamically. ...
Inspiration of reasoning over knowledge graph in consultation system can be drew from those works. ...
arXiv:2106.07502v1
fatcat:qwfzrhme6rfefe4idvk2do422y
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