Imagen de Portada

Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Nitschke, Jannik, Wegmeth, Lukas, Beel, Joeran
Formato:	Preprint
Publicado:	2026
Materias:	Information Retrieval Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

e-Fold Cross-Validation for Recommender-System Evaluation
por: Baumgart, Moritz, et al.
Publicado: (2024)

EMERS: Energy Meter for Recommender Systems
por: Wegmeth, Lukas, et al.
Publicado: (2024)

Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets
por: Wegmeth, Lukas, et al.
Publicado: (2024)

The Environmental Impact of Ensemble Techniques in Recommender Systems
por: Nitschke, Jannik
Publicado: (2025)

The Potential of AutoML for Recommender Systems
por: Vente, Tobias, et al.
Publicado: (2024)

From Clicks to Carbon: The Environmental Toll of Recommender Systems
por: Vente, Tobias, et al.
Publicado: (2024)

Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
por: Wegmeth, Lukas, et al.
Publicado: (2025)

Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics
por: Decker, Jarne Mathi, et al.
Publicado: (2025)

From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs
por: Beel, Joeran, et al.
Publicado: (2025)

Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance
por: Arabzadeh, Ardalan, et al.
Publicado: (2024)

Evaluating Sakana's AI Scientist: Bold Claims, Mixed Results, and a Promising Future?
por: Beel, Joeran, et al.
Publicado: (2025)

Meta-Learning and Targeted Differential Privacy to Improve the Accuracy-Privacy Trade-off in Recommendations
por: Müllner, Peter, et al.
Publicado: (2026)

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit
por: Zhou, Huixue, et al.
Publicado: (2025)

Evaluating Cost-Accuracy Trade-offs in Multimodal Search Relevance Judgements
por: Terragni, Silvia, et al.
Publicado: (2024)

Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
por: Wegmeth, Lukas, et al.
Publicado: (2024)

Pseudo Label NCF for Sparse OHC Recommendation: Dual Representation Learning and the Separability Accuracy Trade off
por: Barman, Pronob Kumar, et al.
Publicado: (2026)

Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
por: Réda, Clémence, et al.
Publicado: (2026)

EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading
por: Rahmana, Md Mahfujur, et al.
Publicado: (2026)

One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation
por: Bito, Ethan, et al.
Publicado: (2026)

Talos: Optimizing Top-$K$ Accuracy in Recommender Systems
por: Zhang, Shengjia, et al.
Publicado: (2026)

Where to Explore: A Reach and Cost-Aware Approach for Unbiased Data Collection in Recommender Systems
por: Chen, Qiang, et al.
Publicado: (2025)

Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning
por: Cheraghi, Radin, et al.
Publicado: (2025)

APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection
por: Vente, Tobias, et al.
Publicado: (2025)

Continual Recommender Systems
por: Yoo, Hyunsik, et al.
Publicado: (2025)

Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest
por: Ebrahimi, Saeed, et al.
Publicado: (2025)

PolyRecommender: A Multimodal Recommendation System for Polymer Discovery
por: Wang, Xin, et al.
Publicado: (2025)

End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
por: Sun, Zexu, et al.
Publicado: (2024)

Dataset-Agnostic Recommender Systems
por: Wijaya, Tri Kurniawan, et al.
Publicado: (2025)

System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes
por: Agarwal, Arpit, et al.
Publicado: (2024)

Evaluation on Entity Matching in Recommender Systems
por: Huang, Zihan, et al.
Publicado: (2026)

The Unreasonable Effectiveness of Data for Recommender Systems
por: Abdou, Youssef
Publicado: (2026)

Tweedie Regression for Video Recommendation System
por: Zheng, Yan, et al.
Publicado: (2025)

Co-clustering for Federated Recommender System
por: He, Xinrui, et al.
Publicado: (2024)

Neural Click Models for Recommender Systems
por: Shirokikh, Mikhail, et al.
Publicado: (2024)

Generalized Embedding Machines for Recommender Systems
por: Yang, Enneng, et al.
Publicado: (2020)

The Structure-Content Trade-off in Knowledge Graph Retrieval
por: Six, Valentin, et al.
Publicado: (2025)

Modeling Attrition in Recommender Systems with Departing Bandits
por: Ben-Porat, Omer, et al.
Publicado: (2022)

Understanding Distribution Structure on Calibrated Recommendation Systems
por: da Silva, Diego Correa, et al.
Publicado: (2025)

Measuring Recency Bias In Sequential Recommendation Systems
por: Oh, Jeonglyul, et al.
Publicado: (2024)

Incentive-Aware Recommender Systems in Two-Sided Markets
por: Dai, Xiaowu, et al.
Publicado: (2022)