Papers Explainable Recommendation
“Explainable Recommendation” 태그가 달린 논문 94편 · 필터 해제
Interest Networks (iNETs) for Cities: Cross-Platform Insights and Urban Behavior Explanations
Location-Based Social Networks (LBSNs) provide a rich foundation for modeling urban behavior through iNETs (Interest Networks), which capture how user interests are distributed throughout urban spaces. This study compare…
Explainable RecommendationRecommendation SystemsCounterfactual Multi-player Bandits for Explainable Recommendation Diversification
Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''. Recent efforts are dedicated to improving the dive…
counterfactualDiversityExplainable RecommendationRecommendation SystemsHealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models
Seeking dietary guidance often requires navigating complex professional knowledge while accommodating individual health conditions. Knowledge Graphs (KGs) offer structured and interpretable nutritional information, where…
Conversational RecommendationExplainable RecommendationKnowledge GraphsHF4Rec: Human-Like Feedback-Driven Optimization Framework for Explainable Recommendation
Recent advancements in explainable recommendation have greatly bolstered user experience by elucidating the decision-making rationale. However, the existing methods actually fail to provide effective feedback signals for…
Explainable RecommendationLogical ReasoningCoherency Improved Explainable Recommendation via Large Language Model
Explainable recommender systems are designed to elucidate the explanation behind each recommendation, enabling users to comprehend the underlying logic. Previous works perform rating prediction and explanation generation…
Explainable RecommendationExplanation GenerationLanguage ModelingLanguage Modelling+3G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness, and trustworthiness. To provide persona…
Collaborative FilteringExplainable RecommendationLanguage ModelingLanguage Modelling+2Learning to Rank Aspects and Opinions for Comparative Explanations
Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered. This work extends the notion of compar…
Explainable RecommendationLearning-To-RankEnhancing Recommendation Systems with GNNs and Addressing Over-Smoothing
This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which reduces model effectiveness as network …
Collaborative FilteringExplainable RecommendationRecommendation SystemsExplainable CTR Prediction via LLM Reasoning
Recommendation Systems have become integral to modern user experiences, but lack transparency in their decision-making processes. Existing explainable recommendation methods are hindered by reliance on a post-hoc paradig…
Click-Through Rate PredictionDecision MakingExplainable RecommendationExplanation Generation+5Enabling Explainable Recommendation in E-commerce with LLM-powered Product Knowledge Graph
How to leverage large language model's superior capability in e-commerce recommendation has been a hot topic. In this paper, we propose LLM-PKG, an efficient approach that distills the knowledge of LLMs into product know…
Explainable RecommendationHallucinationInterpret the Internal States of Recommendation Model with Sparse Autoencoder
Explainable recommendation systems are important to enhance transparency, accuracy, and fairness. Beyond result-level explanations, model-level interpretations can provide valuable insights that allow developers to optim…
Explainable RecommendationFairnessRecommendation SystemsCollaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs
Owing to the impressive general intelligence of large language models (LLMs), there has been a growing trend to integrate them into recommender systems to gain a more profound insight into human interests and intentions.…
Collaborative FilteringExplainable RecommendationRecommendation SystemsTransfer LearningDisentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between the predicted and ground-truth explanatio…
Explainable RecommendationText GenerationGaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation
Large language model-based explainable recommendation (LLM-based ER) systems show promise in generating human-like explanations for recommendations. However, they face challenges in modeling user-item collaborative prefe…
Explainable RecommendationLanguage ModellingLarge Language ModelMixture-of-ExpertsMAPLE: Enhancing Review Generation with Multi-Aspect Prompt LEarning in Explainable Recommendation
Explainable Recommendation task is designed to receive a pair of user and item and output explanations to justify why an item is recommended to a user. Many models treat review-generation as a proxy of explainable recomm…
DiversityExplainable RecommendationHallucinationLanguage Modeling+5CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs
Knowledge graphs (KGs) have been widely adopted to mitigate data sparsity and address cold-start issues in recommender systems. While existing KGs-based recommendation methods can predict user preferences and demands, th…
Explainable RecommendationGraph Neural NetworkKnowledge GraphsRecommendation Systems+3AOTree: Aspect Order Tree-based Model for Explainable Recommendation
Recent recommender systems aim to provide not only accurate recommendations but also explanations that help users understand them better. However, most existing explainable recommendations only consider the importance of…
Decision MakingExplainable RecommendationRecommendation SystemsLANE: Logic Alignment of Non-tuning Large Language Models and Online Recommendation Systems for Explainable Reason Generation
The explainability of recommendation systems is crucial for enhancing user trust and satisfaction. Leveraging large language models (LLMs) offers new opportunities for comprehensive recommendation logic generation. Howev…
Explainable RecommendationRecommendation SystemsXRec: Large Language Models for Explainable Recommendation
Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adopted approach, but while advanced techniq…
Collaborative FilteringDecision MakingExplainable RecommendationNavigate+2Finetuning Large Language Model for Personalized Ranking
Large Language Models (LLMs) have demonstrated remarkable performance across various domains, motivating researchers to investigate their potential use in recommendation systems. However, directly applying LLMs to recomm…
Explainable RecommendationLanguage ModelingLanguage ModellingLarge Language Model+2