• Corpus ID: 239049399

Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation

@article{Hu2021HierarchicalAE,
  title={Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation},
  author={Yidan Hu and Yong Liu and Chunyan Miao and Gongqi Lin and Yuan Miao},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10358}
}
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 item. In this paper, we propose a novel explanation generation framework, named Hierarchical Aspect-guided explanation Generation (HAG), for explainable recommendation. Specifically, HAG employs a review… 

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References

SHOWING 1-10 OF 41 REFERENCES
Generate Natural Language Explanations for Recommendation
TLDR
This work proposes a hierarchical sequence-to-sequence model (HSS) for personalized explanation generation of free-text natural language explanations for personalized recommendation, and proposes an auto-denoising mechanism based on topical item feature words for sentence generation.
Explainable Recommendation: A Survey and New Perspectives
TLDR
This survey highlights the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why, and provides a two-dimensional taxonomy to classify existing explainable recommendations research.
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
TLDR
A multi-task learning solution for explainable recommendation is developed via a joint tensor factorization that predicts not only a user's preference over a list of items, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation.
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects
TLDR
This work proposes an ‘extractive’ approach to identify review segments which justify users’ intentions and designs two personalized generation models which can generate diverse justifications based on templates extracted from justification histories.
Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network
TLDR
A novel knowledgeenhanced PRG model based on capsule graph neural network (CapsGNN) is proposed, which is the first to utilize knowledge graph for the PRG task and is able to enhance user preference at both aspect and word levels.
Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation
TLDR
A unified dual framework of how to inject the probabilistic duality of the two tasks in the training stage is designed, and a transfer learning based model for preference prediction and review generation is proposed.
TriRank: Review-aware Explainable Recommendation by Modeling Aspects
TLDR
TriRank endows the recommender system with a higher degree of explainability and transparency by modeling aspects in reviews, and allows users to interact with the system through their aspect preferences, assisting users in making informed decisions.
Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations
TLDR
Experimental results show that the model successfully learns representations capable of generating coherent and diverse reviews and discover those aspects that users are more inclined to discuss and bias the generated text toward their personalized aspect preferences.
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network
TLDR
A neural recommendation approach which can utilize useful information from both review content and user-item graphs, and applies attention mechanism to model the importance of these interactions to learn informative user and item representations.
Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation
TLDR
This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information that can not only provide accurate recommendation, but also can accompany each recommended item with novel visual explanations.
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