Learning to Hash for Recommendation: A Survey
Fuente:
arXiv
Saved in:
| Main Authors: | Luo, Fangyuan, Chen, Yankai, Wu, Jun, Li, Tong, Yu, Philip S., Liu, Xue |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
by: Wu, Xinyi, et al.
Published: (2024)
by: Wu, Xinyi, et al.
Published: (2024)
On the Evaluation Metric for Hashing
by: Jiang, Qing-Yuan, et al.
Published: (2019)
by: Jiang, Qing-Yuan, et al.
Published: (2019)
Result Diversification in Search and Recommendation: A Survey
by: Wu, Haolun, et al.
Published: (2022)
by: Wu, Haolun, et al.
Published: (2022)
Collaborative Group-Aware Hashing for Fast Recommender Systems
by: Zhang, Yan, et al.
Published: (2025)
by: Zhang, Yan, et al.
Published: (2025)
A Survey on Deep Text Hashing: Efficient Semantic Text Retrieval with Binary Representation
by: He, Liyang, et al.
Published: (2025)
by: He, Liyang, et al.
Published: (2025)
Hierarchical Locality Sensitive Hashing for Structured Data: A Survey
by: Wu, Wei, et al.
Published: (2022)
by: Wu, Wei, et al.
Published: (2022)
Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing
by: Chen, Yankai, et al.
Published: (2024)
by: Chen, Yankai, et al.
Published: (2024)
Causal Learning for Trustworthy Recommender Systems: A Survey
by: Li, Jin, et al.
Published: (2024)
by: Li, Jin, et al.
Published: (2024)
Foundation Models for Recommender Systems: A Survey and New Perspectives
by: Huang, Chengkai, et al.
Published: (2024)
by: Huang, Chengkai, et al.
Published: (2024)
Multi-Interest Recommendation: A Survey
by: Li, Zihao, et al.
Published: (2025)
by: Li, Zihao, et al.
Published: (2025)
On-Device Recommender Systems: A Comprehensive Survey
by: Yin, Hongzhi, et al.
Published: (2024)
by: Yin, Hongzhi, 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)
by: Liu, Ziwei, et al.
Published: (2025)
Joint Modeling in Recommendations: A Survey
by: Zhao, Xiangyu, et al.
Published: (2025)
by: Zhao, Xiangyu, et al.
Published: (2025)
A Survey on Causal Inference for Recommendation
by: Luo, Huishi, et al.
Published: (2023)
by: Luo, Huishi, et al.
Published: (2023)
DiffHash: Text-Guided Targeted Attack via Diffusion Models against Deep Hashing Image Retrieval
by: Liu, Zechao, et al.
Published: (2025)
by: Liu, Zechao, et al.
Published: (2025)
A Survey on Trustworthy Recommender Systems
by: Ge, Yingqiang, et al.
Published: (2022)
by: Ge, Yingqiang, et al.
Published: (2022)
Fairness and Diversity in Recommender Systems: A Survey
by: Zhao, Yuying, et al.
Published: (2023)
by: Zhao, Yuying, et al.
Published: (2023)
A Survey on Multi-Behavior Sequential Recommendation
by: Chen, Xiaoqing, et al.
Published: (2023)
by: Chen, Xiaoqing, et al.
Published: (2023)
A Survey on Generative Recommendation: Data, Model, and Tasks
by: Hou, Min, et al.
Published: (2025)
by: Hou, Min, et al.
Published: (2025)
Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation
by: Chen, Can, et al.
Published: (2023)
by: Chen, Can, et al.
Published: (2023)
Learning Recommender Systems with Soft Target: A Decoupled Perspective
by: Zhang, Hao, et al.
Published: (2024)
by: Zhang, Hao, et al.
Published: (2024)
Nested Hash Layer: A Plug-and-play Module for Multiple-length Hash Code Learning
by: He, Liyang, et al.
Published: (2024)
by: He, Liyang, et al.
Published: (2024)
Beyond the Trigger: Learning Collaborative Context for Generalizable Trigger-Induced Recommendation
by: Gao, Chen, et al.
Published: (2024)
by: Gao, Chen, et al.
Published: (2024)
A Comprehensive Survey on Retrieval Methods in Recommender Systems
by: Huang, Junjie, et al.
Published: (2024)
by: Huang, Junjie, et al.
Published: (2024)
Sign-Guided Bipartite Graph Hashing for Hamming Space Search
by: Wu, Xueyi
Published: (2024)
by: Wu, Xueyi
Published: (2024)
A Survey on Data-Centric Recommender Systems
by: Lai, Riwei, et al.
Published: (2024)
by: Lai, Riwei, et al.
Published: (2024)
Embedding Compression in Recommender Systems: A Survey
by: Li, Shiwei, et al.
Published: (2024)
by: Li, Shiwei, et al.
Published: (2024)
A Survey on Cross-Domain Sequential Recommendation
by: Chen, Shu, et al.
Published: (2024)
by: Chen, Shu, et al.
Published: (2024)
Automating Personalization: Prompt Optimization for Recommendation Reranking
by: Wang, Chen, et al.
Published: (2025)
by: Wang, Chen, et al.
Published: (2025)
An Efficient Continuous Control Perspective for Reinforcement-Learning-based Sequential Recommendation
by: Wang, Jun, et al.
Published: (2024)
by: Wang, Jun, et al.
Published: (2024)
Collaborative Semantic Alignment in Recommendation Systems
by: Wang, Chen, et al.
Published: (2023)
by: Wang, Chen, et al.
Published: (2023)
A Survey on Sequential Recommendation
by: Pan, Liwei, et al.
Published: (2024)
by: Pan, Liwei, et al.
Published: (2024)
Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
by: Wang, Zongwei, et al.
Published: (2026)
by: Wang, Zongwei, et al.
Published: (2026)
Data Augmentation for Sequential Recommendation: A Survey
by: Dang, Yizhou, et al.
Published: (2024)
by: Dang, Yizhou, et al.
Published: (2024)
Poisoning Attacks against Recommender Systems: A Survey
by: Wang, Zongwei, et al.
Published: (2024)
by: Wang, Zongwei, et al.
Published: (2024)
Interactive Visualization Recommendation with Hier-SUCB
by: Hu, Songwen, et al.
Published: (2025)
by: Hu, Songwen, et al.
Published: (2025)
Matryoshka Representation Learning for Recommendation
by: Lai, Riwei, et al.
Published: (2024)
by: Lai, Riwei, et al.
Published: (2024)
Robust Recommender System: A Survey and Future Directions
by: Zhang, Kaike, et al.
Published: (2023)
by: Zhang, Kaike, et al.
Published: (2023)
Graph and Sequential Neural Networks in Session-based Recommendation: A Survey
by: Li, Zihao, et al.
Published: (2024)
by: Li, Zihao, et al.
Published: (2024)
Graph Contrastive Learning on Multi-label Classification for Recommendations
by: Wu, Jiayang, et al.
Published: (2025)
by: Wu, Jiayang, et al.
Published: (2025)
Similar Items
-
GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
by: Wu, Xinyi, et al.
Published: (2024) -
On the Evaluation Metric for Hashing
by: Jiang, Qing-Yuan, et al.
Published: (2019) -
Result Diversification in Search and Recommendation: A Survey
by: Wu, Haolun, et al.
Published: (2022) -
Collaborative Group-Aware Hashing for Fast Recommender Systems
by: Zhang, Yan, et al.
Published: (2025) -
A Survey on Deep Text Hashing: Efficient Semantic Text Retrieval with Binary Representation
by: He, Liyang, et al.
Published: (2025)