Amazon Book
Emerging16papers using it
2020first seen
The 'Amazon-Book' dataset is a benchmark used to evaluate recommender systems, containing user-item interactions specifically related to books on the Amazon platform.
Papers using Amazon Book (16)
- Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information NetworksIntegrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation SystemsReproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message PassingKGRec: A knowledge graph attention-based model for recommender systemShielded RecRL: Explanation Generation for Recommender Systems without Ranking DegradationResearch on Conversational Recommender System Considering Consumer TypesEnhancing LLM-based Recommendation through Semantic-Aligned
Collaborative KnowledgeContextGNN 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 RecommendationRevisiting Neighborhood-based Link Prediction for Collaborative
FilteringChallenging the Myth of Graph Collaborative Filtering: a Reasoned and
Reproducibility-driven AnalysisLT-OCF: Learnable-Time ODE-based Collaborative FilteringOn Generative Agents in RecommendationTransformer-Empowered Content-Aware Collaborative FilteringPlay to Your Strengths: Collaborative Intelligence of Conventional
Recommender Models and Large Language Models