Papers Recommendation Systems
“Recommendation Systems” 태그가 달린 논문 6,373편 · 필터 해제
Technical Report on the Turba Fertilizer Machine Learning Stack in Morocco
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 SystemsSafety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions
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 SystemsAutonomous DrivingAdaptive Item-based Collaborative Structures via Noise Rescheduling in Diffusion for Generative Recommendation
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 FilteringRecommendation SystemsPairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson
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 SystemsERASE: EaRly bAckpropagation SchEdule for Faster Training of Modern Recommendation Systems
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 SystemsROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation
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 SystemsKairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
Algorithmic news personalization in regional markets often fails because modern deep learning models require massive interaction data while real-world news has a short Time-to-Live (TTL < 48 h) and shallow article pools.…
Representation LearningCollaborative FilteringRecommendation SystemsParameterized Fair Resource Allocation under Diversity Constraints
Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly formulated as an optimization problem w…
Recommendation SystemsTRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation
Graph Neural Networks (GNNs) and Large Language Models (LLMs) have each advanced recommendation systems by modeling structural and semantic signals, respectively. However, integrating their complementary strengths remain…
Recommendation SystemsGraph Neural NetworkA scalable online machine learning approach for Stock Recommendation
Stock recommendation systems face the dual challenge of adapting to rapidly changing market conditions while maintaining low-latency predictions for end users. Traditional batch-trained models fail to capture concept dri…
Recommendation SystemsTransfer LearningSlimPer: Make Personalization Model Slim and Smart
Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, w…
Recommendation SystemsLiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention
Indexer-TopK, the operation to compute the scores and select the top-k candidates, is widely used by sparse attention algorithms in large language models and vector retrieval in recommendation systems and vector database…
Recommendation SystemsAn LLM-powered Agentic Recommendation System for Connected TV Content Discovery
Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topics, breaking news, cultural events, and c…
Recommendation SystemsFeature EngineeringA Statistical Test for the Benefits of Personalizing Interventions
From medicine to marketing to social sciences, the promise of tailoring interventions to individuals is undeniable. However, practical applications force weighing personalization's potential benefits with its possible in…
Recommendation SystemsVideo2Reaction: Mapping Video to Audience Reaction Distribution in the Wild
Understanding and forecasting audience reactions to video content are crucial for improving content creation, recommendation systems, and media analysis. To enable audience reaction prediction and other content engagemen…
Recommendation SystemsBeyond Accuracy: How Humans Evaluate Legally Correct but Socially Controversial Legal Advice from Machines
AI systems are increasingly used to provide legal advice, raising questions about whether laypeople accept guidance from algorithms--especially when that advice is legally correct but socially controversial. We report a …
Recommendation SystemsLegal ReasoningReal-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval
The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training two-tower models typically involve in-batch and/or out-of-batch negati…
Recommendation SystemsGR2 Technical Report
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step disproportionately shapes user engagement and do…
Reinforcement LearningRecommendation SystemsHow Anthropomorphic Language Impacts Public Perceptions of AI
Public discourse about artificial intelligence (AI) often uses anthropomorphic language: language that attributes human capabilities and characteristics to the system. This practice has been criticized for setting mislea…
Recommendation SystemsA Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teaming
We address the problem of online multi-human multi-robot teaming through the lens of a linear matching bandit framework, where a learner assigns robots with unknown features from a fixed pool to distinct sets of human ag…
Recommendation Systems