Recommendation Systems

55개 벤치마크 · 논문 6,373편 · 이 태스크의 논문 보기 →

Benchmarks

Most implemented

Semi-Supervised Classification with Graph Convolutional Networks

2016-09-09 · 구현 55개

Neural Collaborative Filtering

2017-08-16 · 구현 43개

Wide & Deep Learning for Recommender Systems

2016-06-24 · 구현 39개

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

2019-05-23 · 구현 31개

Session-based Recommendations with Recurrent Neural Networks

2015-11-21 · 구현 25개

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

2017-03-13 · 구현 23개

Papers

Technical Report on the Turba Fertilizer Machine Learning Stack in Morocco

2026-10-05 · Abdelghani Belgaid, Zakaria Mahmoud, Fahd Chibani, Oumnia Ennaji 외 hf

Site-specific fertilizer recommendation systems adapt nutrient advice to location, soil properties, crop type, and production targets, but scientific reuse is constrained when recommendation functions remain accessible m…

Recommendation Systems

Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions

2026-08-27 · Spyros Dragazis, Aldo Pacchiano arxiv

Contextual bandits are a standard framework for sequential decision-making under uncertainty, with applications in clinical trials, dosage selection, recommendation systems, and autonomous systems. Safety is central in m…

Recommendation Systems Autonomous Driving

Adaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation

2026-08-24 · Jiaqi Wang, Tianying Liu, Heng Chang, Jihong Guan 외 arxiv

Discrete Diffusion Models (DDMs) have recently been introduced to recommendation systems, modeling user history as a token generation process via iterative denoising. However, while effective at capturing user-level sequ…

Collaborative Filtering Recommendation Systems

Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson

2026-08-19 · Tanay Chowdhury, Saeideh Shahrokh Esfahani arxiv

Industrial explainable-recommendation systems built on LLMs incur a substantial serving cost: each request triggers an LLM generation, with latency in the hundreds of milliseconds and cost that scales linearly with traff…

Recommendation Systems

ERASE: EaRly bAckpropagation SchEdule for Faster Training of Modern Recommendation Systems

2026-08-19 · Ergan Shang, Flavio Sales Truzzi arxiv

Lightweight proxy models enable rapid experimentation without repeatedly training frontier-scale systems, but their small kernels often leave modern accelerators underutilized. Conventional training compounds this ineffi…

Recommendation Systems

ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation

2026-07-30 · Yuxin Chen, Liang Luo, Buyun Zhang, Jian Jiao 외 arxiv

Modern recommendation models gain prediction quality by scaling feature-interaction and sequence modules, but production cost constraints cap how far systems can scale. In this work, we propose Request-Oriented Compute S…

Recommendation Systems

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