Aspect-guided syntax graph learning for explainable recommendation

Aspect-guided syntax graph learning for explainable recommendation

Explainable recommendation systems provide explanations for recommendation results to improve their transparency and persuasiveness. The existing explainable recommendation methods generate textual explanations without explicitly considering the user's preferences on different aspects of the it...

Saved in:

書目詳細資料
Main Authors:	Hu, Yidan, Liu, Yong, Miao, Chunyan, Lin, Gongqi, Miao, Yuan
其他作者:	School of Computer Science and Engineering
格式:	Article
語言:	English
出版:	2023
主題:	Engineering::Computer science and engineering Explanation Generation Explainable Recommendation
在線閱讀:	https://hdl.handle.net/10356/164142
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

相似書籍

Explainable recommendation with comparative constraints on product aspects
由: LE, Trung-Hoang, et al.
出版: (2021)

Mining product textual data for recommendation explanations
由: LE TRUNG HOANG,
出版: (2022)

Reinforced Negative Sampling over Knowledge Graph for Recommendation
由: Xiang Wang, et al.
出版: (2020)

Memory bank augmented long-tail sequential recommendation
由: Hu, Yidan, et al.
出版: (2023)

Building more explainable artificial intelligence with argumentation
由: Zeng, Zhiwei, et al.
出版: (2020)

Tagcloud-based explanation with feedback for recommender systems
由: Chen, W., et al.
出版: (2014)

EXPLOITING CROSS-CHANNEL INFORMATION FOR PERSONALIZED RECOMMENDATION
由: WANG XIANG
出版: (2019)

Explanation guided contrastive learning for sequential recommendation
由: WANG, Lei, et al.
出版: (2022)

IMPROVING USABILITY OF MACHINE LEARNING MODEL EXPLANATIONS: EXPERIMENTS WITH VISUAL AND VERBAL COGNITIVE LOAD
由: ASHRAF MOHAMMED ABDUL
出版: (2022)

Contextualized graph attention network for recommendation with item knowledge graph
由: Liu, Yong, et al.
出版: (2022)

Sequential recommendation: From representation learning to reasoning
由: WANG, Lei
出版: (2024)

XAI4FL: enhancing spectrum-based fault localization with explainable artificial intelligence
由: RATNADIRA WIDYASARI,, et al.
出版: (2022)

Interactive Path Reasoning on Graph for Conversational Recommendation
由: Wenqiang Lei, et al.
出版: (2020)

Toward explainable deep anomaly detection
由: PANG, Guansong, et al.
出版: (2021)

Is self-explanation better than explaining to a fictitious student when learning from video lectures?
由: Pi, Zhongling, et al.
出版: (2022)

Learning hierarchical review graph representations for recommendation
由: Liu, Yong, et al.
出版: (2022)

A runway exit prediction model with visually explainable machine decisions
由: Woo, Chuan Jie, et al.
出版: (2022)

Disentangled Graph Collaborative Filtering
由: WANG XIANG, et al.
出版: (2020)

On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach
由: CAI, Ruichu, et al.
出版: (2025)

Pre-training graph transformer with multimodal side information for recommendation
由: Liu, Yong, et al.
出版: (2022)

Diversified interactive recommendation with implicit feedback
由: Liu, Yong, et al.
出版: (2020)

Interpretable recommendation based on graph neural networks
由: Tan, Samantha Shu Hua
出版: (2024)

Demystifying AI: bridging the explainability gap in LLMs
由: Chan, Darren Inn Siew
出版: (2024)

Beyond persuasion : towards conversational recommender system with credible explanations
由: QIN, Peixin, et al.
出版: (2024)

Multi modal personalized explanation generation
由: Marantika, Winda Kirana
出版: (2024)

Initialization matters : regularizing manifold-informed initialization for neural recommendation systems
由: Zhang, Yinan, et al.
出版: (2021)

A survey on reinforcement learning for recommender systems
由: Lin, Yuanguo, et al.
出版: (2023)

Explainable AI for medical over-investigation identification
由: Suresh Kumar Rathika
出版: (2024)

Question-attentive review-level explanation for neural rating regression
由: LE, Trung Hoang, et al.
出版: (2024)

Expertise level identification of air traffic controllers through visual measures and explainable AI
由: Lin, Yen-Po
出版: (2025)

MODELING DYNAMIC ASPECTS OF CONTEXT-AWARE RECOMMENDER SYSTEMS
由: THILINA MADUSANKA THANTHRIWATTA
出版: (2021)

Learning personalized itemset mapping for cross-domain recommendation
由: Zhang, Yinan, et al.
出版: (2021)

Coca: Improving and explaining graph neural network-based vulnerability detection systems
由: CAO, Sicong, et al.
出版: (2024)

The skyline of counterfactual explanations for machine learning decision models
由: Wang, Yongjie, et al.
出版: (2022)

Demystifying faulty code: Step-by-step reasoning for explainable fault localization
由: WIDYASARI, Ratnadira, et al.
出版: (2024)

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
由: Wenqiang Lei, et al.
出版: (2020)

Towards Explaining Sequences of Actions in Multi-Agent Deep Reinforcement Learning Models
由: KHAING, Phyo Wai, et al.
出版: (2023)

Deep learning-based side-channel analysis: exploiting vulnerabilities and explaining neural network decisions
由: Yap, Trevor Hong Eng
出版: (2025)

Temporal attention graph-optimized networks for sequential recommendation
由: Pathak, Siddhant
出版: (2024)

Effects of interactive explainability on improving trust in chatbots: an S-O-R approach
由: Do, Diem Quynh, et al.
出版: (2025)