Attention-Guide Walk Model in Heterogeneous Information Network for Multi-Style Recommendation Explanation

Wang, Xin; Wang, Ying; Ling, Yunzhi

doi:10.1609/aaai.v34i04.6095

Cited by 19 publications

(5 citation statements)

References 13 publications

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“…Other than Natural Language Processing (NLP) tasks, the attention mechanism is also a near-ubiquitous method in recommendation tasks used as explanations in some works. [33], [34], [35], [36] However, there are different opinions on whether attention mechanism could be used as a way to explain data [37], [38], [39].…”

Section: Discussion Of Explanation For Recommendation With Attention ...mentioning

confidence: 99%

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

Li¹,

Chen²,

Li³

et al. 2021

Preprint

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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. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path or independently and separately of the recommendation mechanism. In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendation. 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 context to characterize the next purchased item. Extensive evaluations of TMER on two real-world datasets show state-of-the-art performance compared against recent strong baselines.

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Section: Discussion Of Explanation For Recommendation With Attention ...mentioning

confidence: 99%

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

Li¹,

Chen²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Heterogeneous information networks (HINs) can simulate complex interactions in real-world data such as social networks, biological networks, and knowledge graphs. 40 HINs are typically connected with numerous objects (nodes) and meta-relations. They have recently garnered increasing interest from the research community.…”

Section: Related Workmentioning

confidence: 99%

An intelligent Hybrid‐Q Learning clustering approach and resource management within heterogeneous cluster networks based on reinforcement learning

Mughal,

He,

Zhu

et al. 2023

Trans Emerging Tel Tech

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Recently, heterogeneous cluster networks (HCNs) have been the subject of significant research. The nature of the next‐generation HCN environment is decentralized and highly dynamic; optimization techniques cannot quite express the dynamic characteristics of node resource utilization and communication of HCN networks. In this article, we present an intelligent Hybrid‐Q Learning approach (Hybrid QL)‐based clustering approach for IoT and WSN. Using the self‐learning abilities of (HCNs), we propose a model for dynamic accessing systems on nodes and agents that identify the best possible paths and communication over heterogeneous cluster networks using reinforcement learning. In addition to reducing energy consumption, it creates efficient and effective resource utilization and node communication performance. Through increased throughput and link management, the HCN aims to reduce energy consumption. The proposed model is compared to existing approaches based on various scenarios. Finally, the results of the evaluation tasks demonstrate high accuracy, low‐level complexity, fast dynamic response times, and scalability for heterogeneous cluster networks. Our model showed exceptional node allocation efficiency for dynamic IOT environments and WSNs.

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“…In recent years, deep learning and representation learning have attracted extensive attention, and an interpretable recommendation system based on deep learning technology has gradually emerged. Combined with CNN [26][27][28][29][30][31][32], RNN [33], memory network [34], and attention mechanism [35,36], various recommended interpretation modes are generated. Beutel et al [37] introduced the use of contextual information in recurrent neural networks (RNN) to improve the recommendation effect, conducted an experimental analysis of the classic feature extraction method, and applied RNN to enhance the efficiency of the recommendation algorithm.…”

Section: Other Types Of Interpretable Recommendation Modelsmentioning

confidence: 99%

Fast Explainable Recommendation Model by Combining Fine-Grained Sentiment in Review Data

Wang

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of e-commerce, recommendation system has become one of the main tools that assists users in decision-making, enhances user’s experience, and creates economic value. Since it is difficult to explain the implicit features generated by matrix factorization, explainable recommendation system has attracted more and more attention recently. In this paper, we propose an explainable fast recommendation model by combining fine-grained sentiment in review data (FSER, (Fast) Fine-grained Sentiment for Explainable Recommendation). We innovatively construct user-rating matrix, user-aspect sentiment matrix, and item aspect-descriptive word frequency matrix from the review-based data. And the three matrices are reconstructed by matrix factorization method. The reconstructed results of user-aspect sentiment matrix and item aspect-descriptive word frequency matrix can provide explanation for the final recommendation results. Experiments in the Yelp and Public Comment datasets demonstrate that, compared with several classical models, the proposed FSER model is in the optimal recommendation accuracy range and has lower sparseness and higher training efficiency than tensor models or neural network models; furthermore, it can generate explanatory texts and diagrams that have high interpretation quality.

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Attention-Guide Walk Model in Heterogeneous Information Network for Multi-Style Recommendation Explanation

Cited by 19 publications

References 13 publications

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

An intelligent Hybrid‐Q Learning clustering approach and resource management within heterogeneous cluster networks based on reinforcement learning

Fast Explainable Recommendation Model by Combining Fine-Grained Sentiment in Review Data

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