Amazon Reviews
Canonical27papers using it
2018first seen
Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website
Papers using Amazon Reviews (27)
- PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System EvaluationMiniOneRec: An Open-Source Framework for Scaling Generative RecommendationDialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search AssistantsGraph Contrastive Learning on Multi-label Classification for
RecommendationsModel-agnostic post-hoc explainability for recommender systemsMeta-Learning for Cold-Start Personalization in Prompt-Tuned LLMsREGEN: A Dataset and Benchmarks with Natural Language Critiques and NarrativesDeepSentRec: a deep learning-based sentiment-aware product recommendation systemA Hybrid Temporal Recommender System Based on Sliding-Window Weighted Popularity and Elite Evolutionary Discrete Particle Swarm OptimizationHORIZON: A Benchmark for In-the-wild User Behaviour ModelingDeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative RecommendationTowards Trustworthy LLM-Based Recommendation via Rationale IntegrationPixRec: Leveraging Visual Context for Next-Item Prediction in Sequential RecommendationMGFRec: Towards Reinforced Reasoning Recommendation with Multiple Groundings and FeedbackModeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping SettingsSTAR: A Simple Training-free Approach for Recommendations using Large
Language ModelsLearning Distributed Representations from Reviews for Collaborative
FilteringContrastive Graph Prompt-tuning for Cross-domain RecommendationRecommend for a Reason: Unlocking the Power of Unsupervised
Aspect-Sentiment Co-ExtractionTADO: Time-varying Attention with Dual-Optimizer ModelLearning Similarity Preserving Binary Codes for Recommender SystemsLLMRS: Unlocking Potentials of LLM-Based Recommender Systems for
Software PurchaseLEARN: Knowledge Adaptation from Large Language Model to Recommendation
for Practical Industrial ApplicationEnhancing Collaborative Filtering Recommender with Prompt-Based
Sentiment AnalysisImprove Temporal Awareness of LLMs for Sequential RecommendationUserSumBench: A Benchmark Framework for Evaluating User Summarization
ApproachesBeyond Retrieval: Generating Narratives in Conversational Recommender
Systems