Amazon-book
Emerging17papers using it
2020first seen
The 'Amazon-Book' dataset is a benchmark used to evaluate recommender systems, containing user-item interactions specifically related to books.
Papers using Amazon-book (17)
- Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information NetworksIntegrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation SystemsKGRec: A knowledge graph attention-based model for recommender systemEnhancing LLM-based Recommendation through Semantic-Aligned Collaborative KnowledgeBreaking the Information Silo: Semantic Personas for Cross-Domain RecommendationSAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for RecommendationShielded RecRL: Explanation Generation for Recommender Systems without Ranking DegradationResearch on Conversational Recommender System Considering Consumer TypesReproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message PassingContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation)MVIN: Learning Multiview Items for RecommendationCausal Inference for Knowledge Graph based RecommendationNeighborhood-Enhanced Supervised Contrastive Learning for Collaborative FilteringRevisiting Neighborhood-based Link Prediction for Collaborative FilteringOn Generative Agents in RecommendationTransformer-Empowered Content-Aware Collaborative FilteringPlay to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models