Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation

Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research...

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Bibliographic Details
Published inProceedings of ... International Joint Conference on Neural Networks pp. 1 - 8
Main Authors Chen, Xiaocong, Huang, Chaoran, Yao, Lina, Wang, Xianzhi, liu, Wei, Zhang, Wenjie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
Complexity theory
Deep Neural Network
Feeds
Knowledge engineering
Learning (artificial intelligence)
Neural networks
Recommender System
Reinforcement Learning
Symmetric matrices
Training
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Summary:Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. Inspired by knowledge-aware recommendation, we proposed Knowledge-Guided deep Reinforcement learning (KGRL) to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation. This model is implemented upon the actor-critic network framework. It maintains a local knowledge network to guide decision-making and employs the attention mechanism to capture long-term semantics between items. We have conducted comprehensive experiments in a simulated online environment with six public real-world datasets and demonstrated the superiority of our model over several state-of-the-art methods.
ISSN:2161-4407
DOI:10.1109/IJCNN48605.2020.9207010