Last.fm

Last.fm is a dataset used to evaluate recommender systems, containing user listening histories and music preferences to assess the effectiveness of personalized content delivery and recommendation diversity.

Papers using Last.fm (17)

KGRec: A knowledge graph attention-based model for recommender system2026 · 1 citesAn adversarial framework with dual genetic optimization for similarity-aware matrix factorization in recommendation systems2026Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty2026Membership Inference Attack against Large Language Model-based Recommendation Systems: A New Distillation-based Paradigm2025From Time and Place to Preference: LLM-Driven Geo-Temporal Context in Recommendations2025Research on Conversational Recommender System Considering Consumer Types2025CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System2024Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems2020 · 329 citesCausal Inference for Knowledge Graph based Recommendation2022 · 47 citesHierarchical Context enabled Recurrent Neural Network for Recommendation2019 · 29 citesPopularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems2022 · 25 citesOnline Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young2019 · 5 citesWasserstein Autoencoders for Collaborative Filtering2018 · 3 citesDynamic Collaborative Filtering with Compound Poisson Factorization2016 · 2 citesMulti-Output Recommender: Items, Groups and Friends, and Their Mutual Contributing Effects2012ILCR: Item-based Latent Factors for Sparse Collaborative Retrieval2014Multilingual Prompts in LLM-Based Recommenders: Performance Across Languages2024