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Explainable Recommendation

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Showing 51–75 of 94 papers

TitleStatusHype
An Explainable Autoencoder For Collaborative Filtering Recommendation—0
Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System—0
Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems—0
Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System—0
Comparative Explanations of Recommendations—0
Knowledge-grounded Natural Language Recommendation Explanation—0
LANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation—0
Visually Explainable Recommendation—0
CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs—0
LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation—0
MAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation—0
Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation—0
Meta Decision Trees for Explainable Recommendation Systems—0
MMALFM: Explainable Recommendation by Leveraging Reviews and Images—0
Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation—0
Parameterized Explanations for Investor / Company Matching—0
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs—0
PERS: A Personalized and Explainable POI Recommender System—0
Coherency Improved Explainable Recommendation via Large Language Model—0
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation—0
Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation—0
Uncertainty-Aware Explainable Recommendation with Large Language Models—0
REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation—0
A Survey on Trustworthy Recommender Systems—0
Adherence and Constancy in LIME-RS Explanations for Recommendation—0
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