Cover Image

ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jia, Pengyue, Wang, Yejing, Du, Zhaocheng, Zhao, Xiangyu, Wang, Yichao, Chen, Bo, Wang, Wanyu, Guo, Huifeng, Tang, Ruiming
Format:	Preprint
Published:	2024
Subjects:	Information Retrieval Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
by: Jia, Pengyue, et al.
Published: (2024)

Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark
by: Li, Xiaopeng, et al.
Published: (2024)

SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
by: Li, Xiaopeng, et al.
Published: (2024)

Prompt Tuning as User Inherent Profile Inference Machine
by: Lu, Yusheng, et al.
Published: (2024)

SampleLLM: Optimizing Tabular Data Synthesis in Recommendations
by: Gao, Jingtong, et al.
Published: (2025)

Joint Modeling in Recommendations: A Survey
by: Zhao, Xiangyu, et al.
Published: (2025)

LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
by: Gao, Jingtong, et al.
Published: (2024)

LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device Collaboration
by: Zhang, Yingyi, et al.
Published: (2025)

Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery
by: Li, Xiaopeng, et al.
Published: (2026)

HAMUR: Hyper Adapter for Multi-Domain Recommendation
by: Li, Xiaopeng, et al.
Published: (2023)

A Survey of Personalization: From RAG to Agent
by: Li, Xiaopeng, et al.
Published: (2025)

Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing
by: Zhang, Wenlin, et al.
Published: (2026)

Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
by: Zhang, Wenlin, et al.
Published: (2025)

LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation
by: Wang, Yuhao, et al.
Published: (2024)

Deep Research: A Survey of Autonomous Research Agents
by: Zhang, Wenlin, et al.
Published: (2025)

FairFS: Addressing Deep Feature Selection Biases for Recommender System
by: Wang, Xianquan, et al.
Published: (2026)

LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training
by: Liu, Ziwei, et al.
Published: (2025)

TayFCS: Towards Light Feature Combination Selection for Deep Recommender Systems
by: Wang, Xianquan, et al.
Published: (2025)

Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation
by: Zhao, Yuang, et al.
Published: (2024)

LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations
by: Zhang, Wenlin, et al.
Published: (2024)

ERASE -- A Real-World Aligned Benchmark for Unlearning in Recommender Systems
by: Lubitzsch, Pierre, et al.
Published: (2026)

A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
by: Fu, Zichuan, et al.
Published: (2023)

All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
by: Chen, Bo, et al.
Published: (2024)

Embedding Compression in Recommender Systems: A Survey
by: Li, Shiwei, et al.
Published: (2024)

Large Language Model Enhanced Recommender Systems: A Survey
by: Liu, Qidong, et al.
Published: (2024)

Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models
by: Wang, Yuhao, et al.
Published: (2024)

No One Left Behind: How to Exploit the Incomplete and Skewed Multi-Label Data for Conversion Rate Prediction
by: Jia, Qinglin, et al.
Published: (2025)

LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
by: Liu, Qidong, et al.
Published: (2024)

The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation
by: Liu, Ziwei, et al.
Published: (2025)

Inference Computation Scaling for Feature Augmentation in Recommendation Systems
by: Liu, Weihao, et al.
Published: (2025)

RAGR: Review-Augmented Generative Recommendation
by: Zhang, Yingyi, et al.
Published: (2026)

Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
by: Wang, Hao, et al.
Published: (2025)

Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
by: Zhang, Yingyi, et al.
Published: (2025)

ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation
by: Chen, Jizheng, et al.
Published: (2024)

Conditional Memory Enhanced Item Representation for Generative Recommendation
by: Liu, Ziwei, et al.
Published: (2026)

Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation
by: Wen, Yi, et al.
Published: (2025)

Multimodal Recommender Systems: A Survey
by: Liu, Qidong, et al.
Published: (2023)

LLM4Tag: Automatic Tagging System for Information Retrieval via Large Language Models
by: Tang, Ruiming, et al.
Published: (2025)

From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space
by: Jia, Pengyue, et al.
Published: (2026)

M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
by: Zhu, Jiachen, et al.
Published: (2024)